{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "initial_id",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Config:\n",
      "{\n",
      "    database: Datasets\n",
      "    dataset: cifar10\n",
      "    n_classes: 10\n",
      "    rescale_size: 32\n",
      "    crop_size: 32\n",
      "    cfg_file: ./config/cifar10.cfg\n",
      "    synthetic_data: cifar80no\n",
      "    noise_type: symmetric\n",
      "    closeset_ratio: 0.2\n",
      "    r_ood: 0.2\n",
      "    r_imb: 0.1\n",
      "    gpu: 0\n",
      "    net: cnn\n",
      "    batch_size: 128\n",
      "    lr: 0.001\n",
      "    lr_decay: cosine\n",
      "    weight_decay: 1e-05\n",
      "    opt: adam\n",
      "    warmup_epochs: 5\n",
      "    warmup_lr_scale: 10.0\n",
      "    epochs: 150\n",
      "    save_model: False\n",
      "    use_fp16: False\n",
      "    use_grad_accumulate: False\n",
      "    project: \n",
      "    log: PENIOC\n",
      "    epsilon: 0.5\n",
      "    temperature: 0.1\n",
      "    eta: 0.5\n",
      "    alpha: 0.0\n",
      "    beta: 1.0\n",
      "    gamma: 1.0\n",
      "    omega: 0.1\n",
      "    rho: 1.0\n",
      "    loss_func_aux: mae\n",
      "    weighting: soft\n",
      "    neg_cons: False\n",
      "    activation: tanh\n",
      "    ablation: False\n",
      "    log_freq: 1\n",
      "    asym: False\n",
      "}\n",
      "\n",
      "Available GPUs Index : 0\n",
      "using CIFAR-10...\n",
      "Built imbalanced dataset, r_imb=0.1\n",
      "Mixing in OOD noise, r_ood=0.2\n",
      "Mixing in ID noise, r_id=0.2\n",
      "using CIFAR-10...\n",
      "\u001b[32m2024-10-06 14:25:18,673 - INFO - Categories: 10, Training Samples: 20431, Testing Samples: 10000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:25:18,673 - INFO - Optimizer: adam\u001b[0m\n",
      "\u001b[32m2024-10-06 14:25:18,673 - INFO - Accumulate gradients every 1 iterations --> Acutal batch size is 128\u001b[0m\n",
      "\u001b[32m2024-10-06 14:25:24,498 - INFO - Evaluate Summary Time 1.74s\tLoss 2.1423\t Acc@1 24.3500\t Acc@5 70.1100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:25:24,498 - INFO - Head 67.067\tMid 7.825\tTail 3.667\u001b[0m\n",
      "\u001b[32m2024-10-06 14:25:24,499 - INFO - epoch:   1 | train loss: 2.3362 | train accuracy: 23.680 | test loss: 2.1423 | test accuracy: 24.350 | epoch runtime:   5.82 sec | best accuracy: 24.350 @ epoch: 001\u001b[0m\n",
      "\u001b[32m2024-10-06 14:25:29,500 - INFO - Evaluate Summary Time 1.70s\tLoss 2.0404\t Acc@1 32.1400\t Acc@5 80.9500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:25:29,500 - INFO - Head 66.367\tMid 28.650\tTail 2.567\u001b[0m\n",
      "\u001b[32m2024-10-06 14:25:29,500 - INFO - epoch:   2 | train loss: 2.2490 | train accuracy: 32.808 | test loss: 2.0404 | test accuracy: 32.140 | epoch runtime:   5.00 sec | best accuracy: 32.140 @ epoch: 002\u001b[0m\n",
      "\u001b[32m2024-10-06 14:25:34,489 - INFO - Evaluate Summary Time 1.72s\tLoss 2.0135\t Acc@1 33.3500\t Acc@5 80.3100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:25:34,489 - INFO - Head 73.767\tMid 23.400\tTail 6.200\u001b[0m\n",
      "\u001b[32m2024-10-06 14:25:34,490 - INFO - epoch:   3 | train loss: 2.2276 | train accuracy: 35.970 | test loss: 2.0135 | test accuracy: 33.350 | epoch runtime:   4.99 sec | best accuracy: 33.350 @ epoch: 003\u001b[0m\n",
      "\u001b[32m2024-10-06 14:25:39,540 - INFO - Evaluate Summary Time 1.72s\tLoss 1.9993\t Acc@1 36.4800\t Acc@5 84.0400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:25:39,541 - INFO - Head 69.800\tMid 35.900\tTail 3.933\u001b[0m\n",
      "\u001b[32m2024-10-06 14:25:39,541 - INFO - epoch:   4 | train loss: 2.2119 | train accuracy: 38.353 | test loss: 1.9993 | test accuracy: 36.480 | epoch runtime:   5.05 sec | best accuracy: 36.480 @ epoch: 004\u001b[0m\n",
      "\u001b[32m2024-10-06 14:25:44,534 - INFO - Evaluate Summary Time 1.74s\tLoss 1.8524\t Acc@1 43.5000\t Acc@5 88.7100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:25:44,535 - INFO - Head 72.800\tMid 45.025\tTail 12.167\u001b[0m\n",
      "\u001b[32m2024-10-06 14:25:44,535 - INFO - epoch:   5 | train loss: 2.2010 | train accuracy: 40.106 | test loss: 1.8524 | test accuracy: 43.500 | epoch runtime:   4.99 sec | best accuracy: 43.500 @ epoch: 005\u001b[0m\n",
      "\u001b[32m2024-10-06 14:25:50,151 - INFO - Evaluate Summary Time 1.70s\tLoss 1.9416\t Acc@1 44.3500\t Acc@5 88.4000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:25:50,151 - INFO - Head 78.600\tMid 39.825\tTail 16.133\u001b[0m\n",
      "\u001b[32m2024-10-06 14:25:50,152 - INFO - epoch:   6 | train loss: 2.7992 | train accuracy: 43.297 | test loss: 1.9416 | test accuracy: 44.350 | epoch runtime:   5.62 sec | best accuracy: 44.350 @ epoch: 006\u001b[0m\n",
      "\u001b[32m2024-10-06 14:25:55,812 - INFO - Evaluate Summary Time 1.78s\tLoss 1.9228\t Acc@1 45.2100\t Acc@5 88.6800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:25:55,813 - INFO - Head 79.867\tMid 40.600\tTail 16.700\u001b[0m\n",
      "\u001b[32m2024-10-06 14:25:55,813 - INFO - epoch:   7 | train loss: 2.7689 | train accuracy: 43.850 | test loss: 1.9228 | test accuracy: 45.210 | epoch runtime:   5.66 sec | best accuracy: 45.210 @ epoch: 007\u001b[0m\n",
      "\u001b[32m2024-10-06 14:26:01,284 - INFO - Evaluate Summary Time 1.65s\tLoss 1.9077\t Acc@1 45.9700\t Acc@5 89.2400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:26:01,284 - INFO - Head 78.967\tMid 44.100\tTail 15.467\u001b[0m\n",
      "\u001b[32m2024-10-06 14:26:01,284 - INFO - epoch:   8 | train loss: 2.7561 | train accuracy: 44.525 | test loss: 1.9077 | test accuracy: 45.970 | epoch runtime:   5.47 sec | best accuracy: 45.970 @ epoch: 008\u001b[0m\n",
      "\u001b[32m2024-10-06 14:26:06,954 - INFO - Evaluate Summary Time 1.78s\tLoss 1.8997\t Acc@1 47.5300\t Acc@5 89.6300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:26:06,955 - INFO - Head 79.400\tMid 46.350\tTail 17.233\u001b[0m\n",
      "\u001b[32m2024-10-06 14:26:06,955 - INFO - epoch:   9 | train loss: 2.7510 | train accuracy: 44.878 | test loss: 1.8997 | test accuracy: 47.530 | epoch runtime:   5.67 sec | best accuracy: 47.530 @ epoch: 009\u001b[0m\n",
      "\u001b[32m2024-10-06 14:26:12,491 - INFO - Evaluate Summary Time 1.69s\tLoss 1.8736\t Acc@1 48.0300\t Acc@5 90.3900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:26:12,491 - INFO - Head 80.133\tMid 45.900\tTail 18.767\u001b[0m\n",
      "\u001b[32m2024-10-06 14:26:12,492 - INFO - epoch:  10 | train loss: 2.7477 | train accuracy: 45.485 | test loss: 1.8736 | test accuracy: 48.030 | epoch runtime:   5.54 sec | best accuracy: 48.030 @ epoch: 010\u001b[0m\n",
      "\u001b[32m2024-10-06 14:26:18,247 - INFO - Evaluate Summary Time 1.80s\tLoss 1.8751\t Acc@1 48.3500\t Acc@5 90.7900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:26:18,247 - INFO - Head 79.900\tMid 46.875\tTail 18.767\u001b[0m\n",
      "\u001b[32m2024-10-06 14:26:18,247 - INFO - epoch:  11 | train loss: 2.7449 | train accuracy: 45.685 | test loss: 1.8751 | test accuracy: 48.350 | epoch runtime:   5.76 sec | best accuracy: 48.350 @ epoch: 011\u001b[0m\n",
      "\u001b[32m2024-10-06 14:26:23,795 - INFO - Evaluate Summary Time 1.81s\tLoss 1.8466\t Acc@1 49.7100\t Acc@5 91.2800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:26:23,796 - INFO - Head 80.267\tMid 46.550\tTail 23.367\u001b[0m\n",
      "\u001b[32m2024-10-06 14:26:23,796 - INFO - epoch:  12 | train loss: 2.7410 | train accuracy: 46.527 | test loss: 1.8466 | test accuracy: 49.710 | epoch runtime:   5.55 sec | best accuracy: 49.710 @ epoch: 012\u001b[0m\n",
      "\u001b[32m2024-10-06 14:26:29,292 - INFO - Evaluate Summary Time 1.71s\tLoss 1.8087\t Acc@1 51.4600\t Acc@5 91.9000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:26:29,293 - INFO - Head 82.367\tMid 46.875\tTail 26.667\u001b[0m\n",
      "\u001b[32m2024-10-06 14:26:29,293 - INFO - epoch:  13 | train loss: 2.7374 | train accuracy: 47.076 | test loss: 1.8087 | test accuracy: 51.460 | epoch runtime:   5.50 sec | best accuracy: 51.460 @ epoch: 013\u001b[0m\n",
      "\u001b[32m2024-10-06 14:26:34,773 - INFO - Evaluate Summary Time 1.68s\tLoss 1.8076\t Acc@1 52.2900\t Acc@5 91.3600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:26:34,773 - INFO - Head 82.833\tMid 48.425\tTail 26.900\u001b[0m\n",
      "\u001b[32m2024-10-06 14:26:34,774 - INFO - epoch:  14 | train loss: 2.7337 | train accuracy: 47.731 | test loss: 1.8076 | test accuracy: 52.290 | epoch runtime:   5.48 sec | best accuracy: 52.290 @ epoch: 014\u001b[0m\n",
      "\u001b[32m2024-10-06 14:26:40,290 - INFO - Evaluate Summary Time 1.73s\tLoss 1.7712\t Acc@1 53.9000\t Acc@5 92.2500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:26:40,290 - INFO - Head 82.933\tMid 49.625\tTail 30.567\u001b[0m\n",
      "\u001b[32m2024-10-06 14:26:40,291 - INFO - epoch:  15 | train loss: 2.7303 | train accuracy: 48.759 | test loss: 1.7712 | test accuracy: 53.900 | epoch runtime:   5.52 sec | best accuracy: 53.900 @ epoch: 015\u001b[0m\n",
      "\u001b[32m2024-10-06 14:26:45,948 - INFO - Evaluate Summary Time 1.82s\tLoss 1.7576\t Acc@1 55.7700\t Acc@5 92.3100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:26:45,949 - INFO - Head 83.567\tMid 50.925\tTail 34.433\u001b[0m\n",
      "\u001b[32m2024-10-06 14:26:45,949 - INFO - epoch:  16 | train loss: 2.7257 | train accuracy: 49.567 | test loss: 1.7576 | test accuracy: 55.770 | epoch runtime:   5.66 sec | best accuracy: 55.770 @ epoch: 016\u001b[0m\n",
      "\u001b[32m2024-10-06 14:26:51,510 - INFO - Evaluate Summary Time 1.73s\tLoss 1.7496\t Acc@1 57.7800\t Acc@5 93.1000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:26:51,511 - INFO - Head 84.000\tMid 48.875\tTail 43.433\u001b[0m\n",
      "\u001b[32m2024-10-06 14:26:51,511 - INFO - epoch:  17 | train loss: 2.7215 | train accuracy: 50.477 | test loss: 1.7496 | test accuracy: 57.780 | epoch runtime:   5.56 sec | best accuracy: 57.780 @ epoch: 017\u001b[0m\n",
      "\u001b[32m2024-10-06 14:26:57,142 - INFO - Evaluate Summary Time 1.69s\tLoss 1.7132\t Acc@1 59.8200\t Acc@5 94.1400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:26:57,142 - INFO - Head 81.300\tMid 57.125\tTail 41.933\u001b[0m\n",
      "\u001b[32m2024-10-06 14:26:57,143 - INFO - epoch:  18 | train loss: 2.7199 | train accuracy: 50.986 | test loss: 1.7132 | test accuracy: 59.820 | epoch runtime:   5.63 sec | best accuracy: 59.820 @ epoch: 018\u001b[0m\n",
      "\u001b[32m2024-10-06 14:27:02,657 - INFO - Evaluate Summary Time 1.67s\tLoss 1.7074\t Acc@1 59.6000\t Acc@5 93.7300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:27:02,658 - INFO - Head 83.367\tMid 56.500\tTail 39.967\u001b[0m\n",
      "\u001b[32m2024-10-06 14:27:02,658 - INFO - epoch:  19 | train loss: 2.7155 | train accuracy: 51.853 | test loss: 1.7074 | test accuracy: 59.600 | epoch runtime:   5.52 sec | best accuracy: 59.820 @ epoch: 018\u001b[0m\n",
      "\u001b[32m2024-10-06 14:27:08,288 - INFO - Evaluate Summary Time 1.71s\tLoss 1.7526\t Acc@1 53.5200\t Acc@5 91.8000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:27:08,288 - INFO - Head 81.433\tMid 47.600\tTail 33.500\u001b[0m\n",
      "\u001b[32m2024-10-06 14:27:08,289 - INFO - epoch:  20 | train loss: 2.7122 | train accuracy: 53.150 | test loss: 1.7526 | test accuracy: 53.520 | epoch runtime:   5.63 sec | best accuracy: 59.820 @ epoch: 018\u001b[0m\n",
      "\u001b[32m2024-10-06 14:27:13,912 - INFO - Evaluate Summary Time 1.74s\tLoss 1.7285\t Acc@1 59.8500\t Acc@5 92.5200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:27:13,913 - INFO - Head 83.667\tMid 56.450\tTail 40.567\u001b[0m\n",
      "\u001b[32m2024-10-06 14:27:13,913 - INFO - epoch:  21 | train loss: 2.7099 | train accuracy: 53.835 | test loss: 1.7285 | test accuracy: 59.850 | epoch runtime:   5.62 sec | best accuracy: 59.850 @ epoch: 021\u001b[0m\n",
      "\u001b[32m2024-10-06 14:27:19,403 - INFO - Evaluate Summary Time 1.69s\tLoss 1.6816\t Acc@1 62.7400\t Acc@5 94.0800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:27:19,403 - INFO - Head 81.467\tMid 61.425\tTail 45.767\u001b[0m\n",
      "\u001b[32m2024-10-06 14:27:19,403 - INFO - epoch:  22 | train loss: 2.7077 | train accuracy: 54.687 | test loss: 1.6816 | test accuracy: 62.740 | epoch runtime:   5.49 sec | best accuracy: 62.740 @ epoch: 022\u001b[0m\n",
      "\u001b[32m2024-10-06 14:27:24,955 - INFO - Evaluate Summary Time 1.70s\tLoss 1.6730\t Acc@1 60.4900\t Acc@5 93.2100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:27:24,956 - INFO - Head 84.800\tMid 54.800\tTail 43.767\u001b[0m\n",
      "\u001b[32m2024-10-06 14:27:24,956 - INFO - epoch:  23 | train loss: 2.7050 | train accuracy: 55.523 | test loss: 1.6730 | test accuracy: 60.490 | epoch runtime:   5.55 sec | best accuracy: 62.740 @ epoch: 022\u001b[0m\n",
      "\u001b[32m2024-10-06 14:27:30,602 - INFO - Evaluate Summary Time 1.70s\tLoss 1.6607\t Acc@1 64.1600\t Acc@5 93.4100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:27:30,602 - INFO - Head 84.300\tMid 57.225\tTail 53.267\u001b[0m\n",
      "\u001b[32m2024-10-06 14:27:30,602 - INFO - epoch:  24 | train loss: 2.7021 | train accuracy: 56.644 | test loss: 1.6607 | test accuracy: 64.160 | epoch runtime:   5.65 sec | best accuracy: 64.160 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:27:36,261 - INFO - Evaluate Summary Time 1.75s\tLoss 1.6051\t Acc@1 63.7800\t Acc@5 94.0300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:27:36,261 - INFO - Head 82.300\tMid 60.425\tTail 49.733\u001b[0m\n",
      "\u001b[32m2024-10-06 14:27:36,261 - INFO - epoch:  25 | train loss: 2.6980 | train accuracy: 57.520 | test loss: 1.6051 | test accuracy: 63.780 | epoch runtime:   5.66 sec | best accuracy: 64.160 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:27:41,780 - INFO - Evaluate Summary Time 1.67s\tLoss 1.6539\t Acc@1 63.0200\t Acc@5 93.0100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:27:41,781 - INFO - Head 86.733\tMid 54.275\tTail 50.967\u001b[0m\n",
      "\u001b[32m2024-10-06 14:27:41,781 - INFO - epoch:  26 | train loss: 2.6957 | train accuracy: 58.548 | test loss: 1.6539 | test accuracy: 63.020 | epoch runtime:   5.52 sec | best accuracy: 64.160 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:27:47,348 - INFO - Evaluate Summary Time 1.66s\tLoss 1.6556\t Acc@1 62.0500\t Acc@5 93.5000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:27:47,349 - INFO - Head 85.667\tMid 56.350\tTail 46.033\u001b[0m\n",
      "\u001b[32m2024-10-06 14:27:47,349 - INFO - epoch:  27 | train loss: 2.6923 | train accuracy: 59.131 | test loss: 1.6556 | test accuracy: 62.050 | epoch runtime:   5.57 sec | best accuracy: 64.160 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:27:52,949 - INFO - Evaluate Summary Time 1.77s\tLoss 1.5923\t Acc@1 65.0300\t Acc@5 93.0000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:27:52,949 - INFO - Head 83.433\tMid 57.675\tTail 56.433\u001b[0m\n",
      "\u001b[32m2024-10-06 14:27:52,950 - INFO - epoch:  28 | train loss: 2.6887 | train accuracy: 60.247 | test loss: 1.5923 | test accuracy: 65.030 | epoch runtime:   5.60 sec | best accuracy: 65.030 @ epoch: 028\u001b[0m\n",
      "\u001b[32m2024-10-06 14:27:58,571 - INFO - Evaluate Summary Time 1.70s\tLoss 1.5702\t Acc@1 65.3900\t Acc@5 93.8000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:27:58,572 - INFO - Head 82.300\tMid 61.075\tTail 54.233\u001b[0m\n",
      "\u001b[32m2024-10-06 14:27:58,572 - INFO - epoch:  29 | train loss: 2.6836 | train accuracy: 61.289 | test loss: 1.5702 | test accuracy: 65.390 | epoch runtime:   5.62 sec | best accuracy: 65.390 @ epoch: 029\u001b[0m\n",
      "\u001b[32m2024-10-06 14:28:04,169 - INFO - Evaluate Summary Time 1.85s\tLoss 1.5140\t Acc@1 66.5600\t Acc@5 93.6500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:28:04,169 - INFO - Head 82.600\tMid 61.725\tTail 56.967\u001b[0m\n",
      "\u001b[32m2024-10-06 14:28:04,169 - INFO - epoch:  30 | train loss: 2.6799 | train accuracy: 62.263 | test loss: 1.5140 | test accuracy: 66.560 | epoch runtime:   5.60 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:28:09,626 - INFO - Evaluate Summary Time 1.59s\tLoss 1.5134\t Acc@1 66.1400\t Acc@5 93.3600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:28:09,627 - INFO - Head 81.333\tMid 61.625\tTail 56.967\u001b[0m\n",
      "\u001b[32m2024-10-06 14:28:09,627 - INFO - epoch:  31 | train loss: 2.6742 | train accuracy: 62.860 | test loss: 1.5134 | test accuracy: 66.140 | epoch runtime:   5.46 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:28:15,170 - INFO - Evaluate Summary Time 1.67s\tLoss 1.4921\t Acc@1 66.0200\t Acc@5 93.7100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:28:15,170 - INFO - Head 85.000\tMid 61.125\tTail 53.567\u001b[0m\n",
      "\u001b[32m2024-10-06 14:28:15,170 - INFO - epoch:  32 | train loss: 2.6687 | train accuracy: 64.294 | test loss: 1.4921 | test accuracy: 66.020 | epoch runtime:   5.54 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:28:20,593 - INFO - Evaluate Summary Time 1.58s\tLoss 1.5753\t Acc@1 63.6500\t Acc@5 91.6000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:28:20,593 - INFO - Head 84.800\tMid 54.425\tTail 54.800\u001b[0m\n",
      "\u001b[32m2024-10-06 14:28:20,594 - INFO - epoch:  33 | train loss: 2.6643 | train accuracy: 65.665 | test loss: 1.5753 | test accuracy: 63.650 | epoch runtime:   5.42 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:28:26,040 - INFO - Evaluate Summary Time 1.68s\tLoss 1.4999\t Acc@1 66.1200\t Acc@5 93.3200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:28:26,040 - INFO - Head 83.800\tMid 62.200\tTail 53.667\u001b[0m\n",
      "\u001b[32m2024-10-06 14:28:26,040 - INFO - epoch:  34 | train loss: 2.6599 | train accuracy: 67.006 | test loss: 1.4999 | test accuracy: 66.120 | epoch runtime:   5.45 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:28:31,416 - INFO - Evaluate Summary Time 1.58s\tLoss 1.5055\t Acc@1 66.4500\t Acc@5 93.1000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:28:31,417 - INFO - Head 82.567\tMid 60.100\tTail 58.800\u001b[0m\n",
      "\u001b[32m2024-10-06 14:28:31,417 - INFO - epoch:  35 | train loss: 2.6554 | train accuracy: 68.264 | test loss: 1.5055 | test accuracy: 66.450 | epoch runtime:   5.38 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:28:37,115 - INFO - Evaluate Summary Time 1.83s\tLoss 1.5260\t Acc@1 65.1100\t Acc@5 92.3400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:28:37,116 - INFO - Head 83.033\tMid 60.525\tTail 53.300\u001b[0m\n",
      "\u001b[32m2024-10-06 14:28:37,116 - INFO - epoch:  36 | train loss: 2.6516 | train accuracy: 69.566 | test loss: 1.5260 | test accuracy: 65.110 | epoch runtime:   5.70 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:28:42,617 - INFO - Evaluate Summary Time 1.68s\tLoss 1.5542\t Acc@1 65.1500\t Acc@5 91.7400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:28:42,617 - INFO - Head 81.200\tMid 61.525\tTail 53.933\u001b[0m\n",
      "\u001b[32m2024-10-06 14:28:42,618 - INFO - epoch:  37 | train loss: 2.6462 | train accuracy: 70.804 | test loss: 1.5542 | test accuracy: 65.150 | epoch runtime:   5.50 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:28:48,142 - INFO - Evaluate Summary Time 1.67s\tLoss 1.5158\t Acc@1 66.3100\t Acc@5 92.7100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:28:48,143 - INFO - Head 82.233\tMid 59.550\tTail 59.400\u001b[0m\n",
      "\u001b[32m2024-10-06 14:28:48,143 - INFO - epoch:  38 | train loss: 2.6422 | train accuracy: 72.121 | test loss: 1.5158 | test accuracy: 66.310 | epoch runtime:   5.53 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:28:53,654 - INFO - Evaluate Summary Time 1.70s\tLoss 1.5341\t Acc@1 64.8800\t Acc@5 91.8800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:28:53,655 - INFO - Head 83.967\tMid 57.675\tTail 55.400\u001b[0m\n",
      "\u001b[32m2024-10-06 14:28:53,655 - INFO - epoch:  39 | train loss: 2.6372 | train accuracy: 73.486 | test loss: 1.5341 | test accuracy: 64.880 | epoch runtime:   5.51 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:28:59,221 - INFO - Evaluate Summary Time 1.68s\tLoss 1.4979\t Acc@1 65.6300\t Acc@5 92.7500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:28:59,221 - INFO - Head 80.133\tMid 61.775\tTail 56.267\u001b[0m\n",
      "\u001b[32m2024-10-06 14:28:59,221 - INFO - epoch:  40 | train loss: 2.6333 | train accuracy: 74.392 | test loss: 1.4979 | test accuracy: 65.630 | epoch runtime:   5.57 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:29:04,764 - INFO - Evaluate Summary Time 1.70s\tLoss 1.4878\t Acc@1 65.3500\t Acc@5 91.8300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:29:04,764 - INFO - Head 81.733\tMid 59.375\tTail 56.933\u001b[0m\n",
      "\u001b[32m2024-10-06 14:29:04,764 - INFO - epoch:  41 | train loss: 2.6282 | train accuracy: 75.831 | test loss: 1.4878 | test accuracy: 65.350 | epoch runtime:   5.54 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:29:10,296 - INFO - Evaluate Summary Time 1.68s\tLoss 1.5198\t Acc@1 63.6200\t Acc@5 91.6900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:29:10,296 - INFO - Head 84.700\tMid 56.625\tTail 51.867\u001b[0m\n",
      "\u001b[32m2024-10-06 14:29:10,296 - INFO - epoch:  42 | train loss: 2.6252 | train accuracy: 77.236 | test loss: 1.5198 | test accuracy: 63.620 | epoch runtime:   5.53 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:29:15,879 - INFO - Evaluate Summary Time 1.77s\tLoss 1.4889\t Acc@1 64.3400\t Acc@5 92.1300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:29:15,879 - INFO - Head 81.633\tMid 62.150\tTail 49.967\u001b[0m\n",
      "\u001b[32m2024-10-06 14:29:15,879 - INFO - epoch:  43 | train loss: 2.6197 | train accuracy: 78.523 | test loss: 1.4889 | test accuracy: 64.340 | epoch runtime:   5.58 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:29:21,400 - INFO - Evaluate Summary Time 1.63s\tLoss 1.4928\t Acc@1 66.0400\t Acc@5 91.5100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:29:21,400 - INFO - Head 82.433\tMid 59.850\tTail 57.900\u001b[0m\n",
      "\u001b[32m2024-10-06 14:29:21,400 - INFO - epoch:  44 | train loss: 2.6167 | train accuracy: 79.360 | test loss: 1.4928 | test accuracy: 66.040 | epoch runtime:   5.52 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:29:26,997 - INFO - Evaluate Summary Time 1.79s\tLoss 1.5218\t Acc@1 63.1100\t Acc@5 90.8900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:29:26,997 - INFO - Head 83.833\tMid 57.925\tTail 49.300\u001b[0m\n",
      "\u001b[32m2024-10-06 14:29:26,997 - INFO - epoch:  45 | train loss: 2.6132 | train accuracy: 80.735 | test loss: 1.5218 | test accuracy: 63.110 | epoch runtime:   5.60 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:29:32,435 - INFO - Evaluate Summary Time 1.64s\tLoss 1.5188\t Acc@1 64.3900\t Acc@5 91.1200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:29:32,436 - INFO - Head 81.167\tMid 57.300\tTail 57.067\u001b[0m\n",
      "\u001b[32m2024-10-06 14:29:32,436 - INFO - epoch:  46 | train loss: 2.6097 | train accuracy: 81.592 | test loss: 1.5188 | test accuracy: 64.390 | epoch runtime:   5.44 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:29:37,876 - INFO - Evaluate Summary Time 1.62s\tLoss 1.4878\t Acc@1 65.0400\t Acc@5 92.0000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:29:37,876 - INFO - Head 83.400\tMid 58.625\tTail 55.233\u001b[0m\n",
      "\u001b[32m2024-10-06 14:29:37,877 - INFO - epoch:  47 | train loss: 2.6051 | train accuracy: 82.996 | test loss: 1.4878 | test accuracy: 65.040 | epoch runtime:   5.44 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:29:43,459 - INFO - Evaluate Summary Time 1.82s\tLoss 1.5530\t Acc@1 61.8800\t Acc@5 90.1000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:29:43,460 - INFO - Head 80.667\tMid 54.450\tTail 53.000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:29:43,460 - INFO - epoch:  48 | train loss: 2.6025 | train accuracy: 83.863 | test loss: 1.5530 | test accuracy: 61.880 | epoch runtime:   5.58 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:29:49,067 - INFO - Evaluate Summary Time 1.75s\tLoss 1.5584\t Acc@1 62.8200\t Acc@5 90.3600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:29:49,068 - INFO - Head 81.933\tMid 55.850\tTail 53.000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:29:49,068 - INFO - epoch:  49 | train loss: 2.5979 | train accuracy: 85.121 | test loss: 1.5584 | test accuracy: 62.820 | epoch runtime:   5.61 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:29:54,696 - INFO - Evaluate Summary Time 1.68s\tLoss 1.4986\t Acc@1 63.4200\t Acc@5 91.0500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:29:54,697 - INFO - Head 82.233\tMid 56.675\tTail 53.600\u001b[0m\n",
      "\u001b[32m2024-10-06 14:29:54,697 - INFO - epoch:  50 | train loss: 2.5943 | train accuracy: 86.095 | test loss: 1.4986 | test accuracy: 63.420 | epoch runtime:   5.63 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:30:00,249 - INFO - Evaluate Summary Time 1.69s\tLoss 1.5050\t Acc@1 64.7600\t Acc@5 91.0800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:30:00,249 - INFO - Head 79.900\tMid 58.600\tTail 57.833\u001b[0m\n",
      "\u001b[32m2024-10-06 14:30:00,250 - INFO - epoch:  51 | train loss: 2.5902 | train accuracy: 86.966 | test loss: 1.5050 | test accuracy: 64.760 | epoch runtime:   5.55 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:30:05,868 - INFO - Evaluate Summary Time 1.75s\tLoss 1.5082\t Acc@1 63.5300\t Acc@5 90.7100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:30:05,868 - INFO - Head 82.000\tMid 58.650\tTail 51.567\u001b[0m\n",
      "\u001b[32m2024-10-06 14:30:05,869 - INFO - epoch:  52 | train loss: 2.5870 | train accuracy: 87.969 | test loss: 1.5082 | test accuracy: 63.530 | epoch runtime:   5.62 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:30:11,411 - INFO - Evaluate Summary Time 1.71s\tLoss 1.5346\t Acc@1 62.1300\t Acc@5 89.8600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:30:11,411 - INFO - Head 83.000\tMid 55.050\tTail 50.700\u001b[0m\n",
      "\u001b[32m2024-10-06 14:30:11,411 - INFO - epoch:  53 | train loss: 2.5830 | train accuracy: 88.821 | test loss: 1.5346 | test accuracy: 62.130 | epoch runtime:   5.54 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:30:17,074 - INFO - Evaluate Summary Time 1.76s\tLoss 1.5234\t Acc@1 63.0600\t Acc@5 90.5900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:30:17,075 - INFO - Head 82.067\tMid 57.450\tTail 51.533\u001b[0m\n",
      "\u001b[32m2024-10-06 14:30:17,075 - INFO - epoch:  54 | train loss: 2.5810 | train accuracy: 89.423 | test loss: 1.5234 | test accuracy: 63.060 | epoch runtime:   5.66 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:30:22,531 - INFO - Evaluate Summary Time 1.72s\tLoss 1.5325\t Acc@1 62.8200\t Acc@5 90.0400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:30:22,531 - INFO - Head 79.833\tMid 59.350\tTail 50.433\u001b[0m\n",
      "\u001b[32m2024-10-06 14:30:22,531 - INFO - epoch:  55 | train loss: 2.5783 | train accuracy: 90.186 | test loss: 1.5325 | test accuracy: 62.820 | epoch runtime:   5.46 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:30:28,169 - INFO - Evaluate Summary Time 1.73s\tLoss 1.5158\t Acc@1 62.6600\t Acc@5 90.3000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:30:28,169 - INFO - Head 80.733\tMid 57.150\tTail 51.933\u001b[0m\n",
      "\u001b[32m2024-10-06 14:30:28,169 - INFO - epoch:  56 | train loss: 2.5743 | train accuracy: 90.926 | test loss: 1.5158 | test accuracy: 62.660 | epoch runtime:   5.64 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:30:33,709 - INFO - Evaluate Summary Time 1.73s\tLoss 1.5387\t Acc@1 61.8400\t Acc@5 89.2800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:30:33,709 - INFO - Head 80.500\tMid 56.450\tTail 50.367\u001b[0m\n",
      "\u001b[32m2024-10-06 14:30:33,710 - INFO - epoch:  57 | train loss: 2.5729 | train accuracy: 91.518 | test loss: 1.5387 | test accuracy: 61.840 | epoch runtime:   5.54 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:30:39,292 - INFO - Evaluate Summary Time 1.71s\tLoss 1.5359\t Acc@1 61.6900\t Acc@5 89.6000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:30:39,292 - INFO - Head 80.700\tMid 57.750\tTail 47.933\u001b[0m\n",
      "\u001b[32m2024-10-06 14:30:39,293 - INFO - epoch:  58 | train loss: 2.5706 | train accuracy: 92.110 | test loss: 1.5359 | test accuracy: 61.690 | epoch runtime:   5.58 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:30:44,775 - INFO - Evaluate Summary Time 1.66s\tLoss 1.5026\t Acc@1 62.9300\t Acc@5 90.9400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:30:44,776 - INFO - Head 81.167\tMid 59.300\tTail 49.533\u001b[0m\n",
      "\u001b[32m2024-10-06 14:30:44,776 - INFO - epoch:  59 | train loss: 2.5671 | train accuracy: 92.658 | test loss: 1.5026 | test accuracy: 62.930 | epoch runtime:   5.48 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:30:50,444 - INFO - Evaluate Summary Time 1.79s\tLoss 1.5297\t Acc@1 62.1500\t Acc@5 90.2200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:30:50,444 - INFO - Head 81.700\tMid 57.625\tTail 48.633\u001b[0m\n",
      "\u001b[32m2024-10-06 14:30:50,444 - INFO - epoch:  60 | train loss: 2.5658 | train accuracy: 93.246 | test loss: 1.5297 | test accuracy: 62.150 | epoch runtime:   5.67 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:30:55,979 - INFO - Evaluate Summary Time 1.70s\tLoss 1.5593\t Acc@1 59.3900\t Acc@5 88.7900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:30:55,980 - INFO - Head 82.300\tMid 48.950\tTail 50.400\u001b[0m\n",
      "\u001b[32m2024-10-06 14:30:55,980 - INFO - epoch:  61 | train loss: 2.5621 | train accuracy: 93.647 | test loss: 1.5593 | test accuracy: 59.390 | epoch runtime:   5.54 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:31:01,355 - INFO - Evaluate Summary Time 1.61s\tLoss 1.5660\t Acc@1 59.8000\t Acc@5 88.9400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:31:01,355 - INFO - Head 77.900\tMid 54.325\tTail 49.000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:31:01,356 - INFO - epoch:  62 | train loss: 2.5607 | train accuracy: 94.024 | test loss: 1.5660 | test accuracy: 59.800 | epoch runtime:   5.38 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:31:06,884 - INFO - Evaluate Summary Time 1.68s\tLoss 1.5236\t Acc@1 61.6900\t Acc@5 90.5200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:31:06,885 - INFO - Head 79.233\tMid 55.550\tTail 52.333\u001b[0m\n",
      "\u001b[32m2024-10-06 14:31:06,885 - INFO - epoch:  63 | train loss: 2.5575 | train accuracy: 94.445 | test loss: 1.5236 | test accuracy: 61.690 | epoch runtime:   5.53 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:31:12,424 - INFO - Evaluate Summary Time 1.65s\tLoss 1.5437\t Acc@1 61.9600\t Acc@5 89.8700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:31:12,424 - INFO - Head 78.067\tMid 55.875\tTail 53.967\u001b[0m\n",
      "\u001b[32m2024-10-06 14:31:12,424 - INFO - epoch:  64 | train loss: 2.5583 | train accuracy: 95.003 | test loss: 1.5437 | test accuracy: 61.960 | epoch runtime:   5.54 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:31:18,015 - INFO - Evaluate Summary Time 1.68s\tLoss 1.5157\t Acc@1 62.1900\t Acc@5 90.1300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:31:18,015 - INFO - Head 80.433\tMid 54.075\tTail 54.767\u001b[0m\n",
      "\u001b[32m2024-10-06 14:31:18,016 - INFO - epoch:  65 | train loss: 2.5560 | train accuracy: 95.086 | test loss: 1.5157 | test accuracy: 62.190 | epoch runtime:   5.59 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:31:23,501 - INFO - Evaluate Summary Time 1.75s\tLoss 1.5255\t Acc@1 61.7800\t Acc@5 90.3800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:31:23,501 - INFO - Head 79.533\tMid 56.475\tTail 51.100\u001b[0m\n",
      "\u001b[32m2024-10-06 14:31:23,501 - INFO - epoch:  66 | train loss: 2.5533 | train accuracy: 95.448 | test loss: 1.5255 | test accuracy: 61.780 | epoch runtime:   5.49 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:31:29,032 - INFO - Evaluate Summary Time 1.65s\tLoss 1.5245\t Acc@1 61.9400\t Acc@5 90.4100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:31:29,032 - INFO - Head 79.733\tMid 55.900\tTail 52.200\u001b[0m\n",
      "\u001b[32m2024-10-06 14:31:29,033 - INFO - epoch:  67 | train loss: 2.5516 | train accuracy: 95.683 | test loss: 1.5245 | test accuracy: 61.940 | epoch runtime:   5.53 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:31:34,642 - INFO - Evaluate Summary Time 1.74s\tLoss 1.5744\t Acc@1 59.0900\t Acc@5 88.2100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:31:34,643 - INFO - Head 77.633\tMid 53.275\tTail 48.300\u001b[0m\n",
      "\u001b[32m2024-10-06 14:31:34,643 - INFO - epoch:  68 | train loss: 2.5484 | train accuracy: 96.124 | test loss: 1.5744 | test accuracy: 59.090 | epoch runtime:   5.61 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:31:40,202 - INFO - Evaluate Summary Time 1.66s\tLoss 1.5641\t Acc@1 60.6700\t Acc@5 88.8200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:31:40,202 - INFO - Head 78.800\tMid 55.525\tTail 49.400\u001b[0m\n",
      "\u001b[32m2024-10-06 14:31:40,202 - INFO - epoch:  69 | train loss: 2.5494 | train accuracy: 96.256 | test loss: 1.5641 | test accuracy: 60.670 | epoch runtime:   5.56 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:31:45,746 - INFO - Evaluate Summary Time 1.74s\tLoss 1.5473\t Acc@1 60.4100\t Acc@5 89.9700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:31:45,747 - INFO - Head 79.800\tMid 54.800\tTail 48.500\u001b[0m\n",
      "\u001b[32m2024-10-06 14:31:45,747 - INFO - epoch:  70 | train loss: 2.5477 | train accuracy: 96.677 | test loss: 1.5473 | test accuracy: 60.410 | epoch runtime:   5.54 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:31:51,139 - INFO - Evaluate Summary Time 1.65s\tLoss 1.5244\t Acc@1 61.4100\t Acc@5 89.6200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:31:51,140 - INFO - Head 80.233\tMid 57.525\tTail 47.767\u001b[0m\n",
      "\u001b[32m2024-10-06 14:31:51,140 - INFO - epoch:  71 | train loss: 2.5461 | train accuracy: 96.549 | test loss: 1.5244 | test accuracy: 61.410 | epoch runtime:   5.39 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:31:56,725 - INFO - Evaluate Summary Time 1.72s\tLoss 1.5521\t Acc@1 60.5900\t Acc@5 89.2300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:31:56,726 - INFO - Head 76.933\tMid 56.150\tTail 50.167\u001b[0m\n",
      "\u001b[32m2024-10-06 14:31:56,726 - INFO - epoch:  72 | train loss: 2.5430 | train accuracy: 96.990 | test loss: 1.5521 | test accuracy: 60.590 | epoch runtime:   5.59 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:32:02,245 - INFO - Evaluate Summary Time 1.63s\tLoss 1.5587\t Acc@1 59.9200\t Acc@5 88.7300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:32:02,245 - INFO - Head 78.933\tMid 55.725\tTail 46.500\u001b[0m\n",
      "\u001b[32m2024-10-06 14:32:02,245 - INFO - epoch:  73 | train loss: 2.5422 | train accuracy: 96.961 | test loss: 1.5587 | test accuracy: 59.920 | epoch runtime:   5.52 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:32:07,820 - INFO - Evaluate Summary Time 1.72s\tLoss 1.5939\t Acc@1 58.3400\t Acc@5 88.3900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:32:07,820 - INFO - Head 78.133\tMid 53.200\tTail 45.400\u001b[0m\n",
      "\u001b[32m2024-10-06 14:32:07,821 - INFO - epoch:  74 | train loss: 2.5395 | train accuracy: 97.342 | test loss: 1.5939 | test accuracy: 58.340 | epoch runtime:   5.58 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:32:13,485 - INFO - Evaluate Summary Time 1.78s\tLoss 1.5535\t Acc@1 60.7200\t Acc@5 89.9100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:32:13,485 - INFO - Head 78.633\tMid 57.150\tTail 47.567\u001b[0m\n",
      "\u001b[32m2024-10-06 14:32:13,485 - INFO - epoch:  75 | train loss: 2.5390 | train accuracy: 97.411 | test loss: 1.5535 | test accuracy: 60.720 | epoch runtime:   5.66 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:32:19,293 - INFO - Evaluate Summary Time 1.67s\tLoss 1.5575\t Acc@1 60.7700\t Acc@5 89.1000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:32:19,293 - INFO - Head 75.900\tMid 56.675\tTail 51.100\u001b[0m\n",
      "\u001b[32m2024-10-06 14:32:19,293 - INFO - epoch:  76 | train loss: 2.5372 | train accuracy: 97.660 | test loss: 1.5575 | test accuracy: 60.770 | epoch runtime:   5.81 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:32:24,945 - INFO - Evaluate Summary Time 1.73s\tLoss 1.5247\t Acc@1 62.0700\t Acc@5 89.7800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:32:24,945 - INFO - Head 78.067\tMid 58.550\tTail 50.767\u001b[0m\n",
      "\u001b[32m2024-10-06 14:32:24,946 - INFO - epoch:  77 | train loss: 2.5377 | train accuracy: 97.758 | test loss: 1.5247 | test accuracy: 62.070 | epoch runtime:   5.65 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:32:30,499 - INFO - Evaluate Summary Time 1.72s\tLoss 1.5486\t Acc@1 60.3500\t Acc@5 89.4900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:32:30,500 - INFO - Head 81.533\tMid 54.450\tTail 47.033\u001b[0m\n",
      "\u001b[32m2024-10-06 14:32:30,500 - INFO - epoch:  78 | train loss: 2.5360 | train accuracy: 97.871 | test loss: 1.5486 | test accuracy: 60.350 | epoch runtime:   5.55 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:32:36,089 - INFO - Evaluate Summary Time 1.72s\tLoss 1.5460\t Acc@1 61.4700\t Acc@5 89.1300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:32:36,089 - INFO - Head 78.967\tMid 57.250\tTail 49.600\u001b[0m\n",
      "\u001b[32m2024-10-06 14:32:36,089 - INFO - epoch:  79 | train loss: 2.5338 | train accuracy: 97.744 | test loss: 1.5460 | test accuracy: 61.470 | epoch runtime:   5.59 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:32:41,655 - INFO - Evaluate Summary Time 1.69s\tLoss 1.5588\t Acc@1 60.1000\t Acc@5 89.0600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:32:41,655 - INFO - Head 79.200\tMid 56.000\tTail 46.467\u001b[0m\n",
      "\u001b[32m2024-10-06 14:32:41,656 - INFO - epoch:  80 | train loss: 2.5331 | train accuracy: 98.174 | test loss: 1.5588 | test accuracy: 60.100 | epoch runtime:   5.57 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:32:47,334 - INFO - Evaluate Summary Time 1.74s\tLoss 1.5389\t Acc@1 61.2500\t Acc@5 89.4700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:32:47,335 - INFO - Head 79.000\tMid 55.525\tTail 51.133\u001b[0m\n",
      "\u001b[32m2024-10-06 14:32:47,335 - INFO - epoch:  81 | train loss: 2.5180 | train accuracy: 98.786 | test loss: 1.5389 | test accuracy: 61.250 | epoch runtime:   5.68 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:32:52,903 - INFO - Evaluate Summary Time 1.70s\tLoss 1.5752\t Acc@1 59.5500\t Acc@5 88.7200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:32:52,904 - INFO - Head 78.500\tMid 55.150\tTail 46.467\u001b[0m\n",
      "\u001b[32m2024-10-06 14:32:52,904 - INFO - epoch:  82 | train loss: 2.5161 | train accuracy: 99.031 | test loss: 1.5752 | test accuracy: 59.550 | epoch runtime:   5.57 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:32:58,617 - INFO - Evaluate Summary Time 1.81s\tLoss 1.5283\t Acc@1 62.0200\t Acc@5 89.7800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:32:58,617 - INFO - Head 80.433\tMid 56.250\tTail 51.300\u001b[0m\n",
      "\u001b[32m2024-10-06 14:32:58,617 - INFO - epoch:  83 | train loss: 2.5124 | train accuracy: 99.232 | test loss: 1.5283 | test accuracy: 62.020 | epoch runtime:   5.71 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:33:04,245 - INFO - Evaluate Summary Time 1.82s\tLoss 1.5412\t Acc@1 61.2900\t Acc@5 89.5000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:33:04,246 - INFO - Head 77.767\tMid 56.650\tTail 51.000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:33:04,246 - INFO - epoch:  84 | train loss: 2.5108 | train accuracy: 99.266 | test loss: 1.5412 | test accuracy: 61.290 | epoch runtime:   5.63 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:33:09,696 - INFO - Evaluate Summary Time 1.61s\tLoss 1.5303\t Acc@1 60.8700\t Acc@5 89.8700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:33:09,697 - INFO - Head 80.800\tMid 53.975\tTail 50.133\u001b[0m\n",
      "\u001b[32m2024-10-06 14:33:09,697 - INFO - epoch:  85 | train loss: 2.5104 | train accuracy: 99.295 | test loss: 1.5303 | test accuracy: 60.870 | epoch runtime:   5.45 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:33:15,145 - INFO - Evaluate Summary Time 1.70s\tLoss 1.5491\t Acc@1 61.0100\t Acc@5 89.1700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:33:15,145 - INFO - Head 78.767\tMid 56.300\tTail 49.533\u001b[0m\n",
      "\u001b[32m2024-10-06 14:33:15,146 - INFO - epoch:  86 | train loss: 2.5097 | train accuracy: 99.369 | test loss: 1.5491 | test accuracy: 61.010 | epoch runtime:   5.45 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:33:20,719 - INFO - Evaluate Summary Time 1.70s\tLoss 1.5509\t Acc@1 60.8000\t Acc@5 89.1900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:33:20,720 - INFO - Head 78.700\tMid 55.675\tTail 49.733\u001b[0m\n",
      "\u001b[32m2024-10-06 14:33:20,720 - INFO - epoch:  87 | train loss: 2.5064 | train accuracy: 99.462 | test loss: 1.5509 | test accuracy: 60.800 | epoch runtime:   5.57 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:33:26,263 - INFO - Evaluate Summary Time 1.69s\tLoss 1.5509\t Acc@1 60.5000\t Acc@5 89.6400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:33:26,263 - INFO - Head 80.500\tMid 55.125\tTail 47.667\u001b[0m\n",
      "\u001b[32m2024-10-06 14:33:26,263 - INFO - epoch:  88 | train loss: 2.5087 | train accuracy: 99.462 | test loss: 1.5509 | test accuracy: 60.500 | epoch runtime:   5.54 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:33:31,732 - INFO - Evaluate Summary Time 1.62s\tLoss 1.5561\t Acc@1 60.4800\t Acc@5 89.2600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:33:31,732 - INFO - Head 79.000\tMid 56.750\tTail 46.933\u001b[0m\n",
      "\u001b[32m2024-10-06 14:33:31,732 - INFO - epoch:  89 | train loss: 2.5074 | train accuracy: 99.432 | test loss: 1.5561 | test accuracy: 60.480 | epoch runtime:   5.47 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:33:37,300 - INFO - Evaluate Summary Time 1.79s\tLoss 1.5490\t Acc@1 60.4600\t Acc@5 89.5600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:33:37,300 - INFO - Head 81.433\tMid 54.925\tTail 46.867\u001b[0m\n",
      "\u001b[32m2024-10-06 14:33:37,301 - INFO - epoch:  90 | train loss: 2.5054 | train accuracy: 99.530 | test loss: 1.5490 | test accuracy: 60.460 | epoch runtime:   5.57 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:33:42,744 - INFO - Evaluate Summary Time 1.58s\tLoss 1.5565\t Acc@1 60.3500\t Acc@5 89.4700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:33:42,745 - INFO - Head 79.300\tMid 54.825\tTail 48.767\u001b[0m\n",
      "\u001b[32m2024-10-06 14:33:42,745 - INFO - epoch:  91 | train loss: 2.5045 | train accuracy: 99.476 | test loss: 1.5565 | test accuracy: 60.350 | epoch runtime:   5.44 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:33:48,379 - INFO - Evaluate Summary Time 1.70s\tLoss 1.5468\t Acc@1 61.0000\t Acc@5 89.4400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:33:48,379 - INFO - Head 79.867\tMid 54.850\tTail 50.333\u001b[0m\n",
      "\u001b[32m2024-10-06 14:33:48,380 - INFO - epoch:  92 | train loss: 2.5051 | train accuracy: 99.604 | test loss: 1.5468 | test accuracy: 61.000 | epoch runtime:   5.63 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:33:53,944 - INFO - Evaluate Summary Time 1.72s\tLoss 1.5427\t Acc@1 60.7000\t Acc@5 89.7700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:33:53,945 - INFO - Head 79.500\tMid 55.525\tTail 48.800\u001b[0m\n",
      "\u001b[32m2024-10-06 14:33:53,945 - INFO - epoch:  93 | train loss: 2.5040 | train accuracy: 99.574 | test loss: 1.5427 | test accuracy: 60.700 | epoch runtime:   5.57 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:33:59,469 - INFO - Evaluate Summary Time 1.63s\tLoss 1.5480\t Acc@1 60.4300\t Acc@5 89.3700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:33:59,469 - INFO - Head 79.300\tMid 55.200\tTail 48.533\u001b[0m\n",
      "\u001b[32m2024-10-06 14:33:59,470 - INFO - epoch:  94 | train loss: 2.5017 | train accuracy: 99.589 | test loss: 1.5480 | test accuracy: 60.430 | epoch runtime:   5.52 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:34:04,913 - INFO - Evaluate Summary Time 1.63s\tLoss 1.5662\t Acc@1 60.1000\t Acc@5 88.9900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:34:04,913 - INFO - Head 79.733\tMid 56.300\tTail 45.533\u001b[0m\n",
      "\u001b[32m2024-10-06 14:34:04,914 - INFO - epoch:  95 | train loss: 2.5014 | train accuracy: 99.657 | test loss: 1.5662 | test accuracy: 60.100 | epoch runtime:   5.44 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:34:10,487 - INFO - Evaluate Summary Time 1.68s\tLoss 1.5588\t Acc@1 60.1500\t Acc@5 89.1400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:34:10,487 - INFO - Head 78.900\tMid 56.175\tTail 46.700\u001b[0m\n",
      "\u001b[32m2024-10-06 14:34:10,487 - INFO - epoch:  96 | train loss: 2.5004 | train accuracy: 99.638 | test loss: 1.5588 | test accuracy: 60.150 | epoch runtime:   5.57 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:34:16,155 - INFO - Evaluate Summary Time 1.80s\tLoss 1.5509\t Acc@1 61.0600\t Acc@5 89.7400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:34:16,156 - INFO - Head 79.833\tMid 55.525\tTail 49.667\u001b[0m\n",
      "\u001b[32m2024-10-06 14:34:16,156 - INFO - epoch:  97 | train loss: 2.4989 | train accuracy: 99.706 | test loss: 1.5509 | test accuracy: 61.060 | epoch runtime:   5.67 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:34:21,665 - INFO - Evaluate Summary Time 1.72s\tLoss 1.5460\t Acc@1 60.5500\t Acc@5 89.5100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:34:21,665 - INFO - Head 80.267\tMid 54.125\tTail 49.400\u001b[0m\n",
      "\u001b[32m2024-10-06 14:34:21,665 - INFO - epoch:  98 | train loss: 2.5001 | train accuracy: 99.731 | test loss: 1.5460 | test accuracy: 60.550 | epoch runtime:   5.51 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:34:27,205 - INFO - Evaluate Summary Time 1.66s\tLoss 1.5573\t Acc@1 60.5800\t Acc@5 89.3400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:34:27,205 - INFO - Head 79.000\tMid 54.025\tTail 50.900\u001b[0m\n",
      "\u001b[32m2024-10-06 14:34:27,205 - INFO - epoch:  99 | train loss: 2.4989 | train accuracy: 99.697 | test loss: 1.5573 | test accuracy: 60.580 | epoch runtime:   5.54 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:34:32,835 - INFO - Evaluate Summary Time 1.72s\tLoss 1.5535\t Acc@1 60.4500\t Acc@5 89.5600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:34:32,836 - INFO - Head 78.867\tMid 56.400\tTail 47.433\u001b[0m\n",
      "\u001b[32m2024-10-06 14:34:32,836 - INFO - epoch: 100 | train loss: 2.4987 | train accuracy: 99.711 | test loss: 1.5535 | test accuracy: 60.450 | epoch runtime:   5.63 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:34:38,447 - INFO - Evaluate Summary Time 1.73s\tLoss 1.5544\t Acc@1 60.2200\t Acc@5 89.7500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:34:38,447 - INFO - Head 78.700\tMid 55.250\tTail 48.367\u001b[0m\n",
      "\u001b[32m2024-10-06 14:34:38,448 - INFO - epoch: 101 | train loss: 2.4982 | train accuracy: 99.765 | test loss: 1.5544 | test accuracy: 60.220 | epoch runtime:   5.61 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:34:43,978 - INFO - Evaluate Summary Time 1.72s\tLoss 1.5385\t Acc@1 61.4800\t Acc@5 89.8700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:34:43,978 - INFO - Head 77.933\tMid 55.150\tTail 53.467\u001b[0m\n",
      "\u001b[32m2024-10-06 14:34:43,978 - INFO - epoch: 102 | train loss: 2.4968 | train accuracy: 99.790 | test loss: 1.5385 | test accuracy: 61.480 | epoch runtime:   5.53 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:34:49,500 - INFO - Evaluate Summary Time 1.71s\tLoss 1.5378\t Acc@1 61.1100\t Acc@5 89.6100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:34:49,500 - INFO - Head 77.433\tMid 57.775\tTail 49.233\u001b[0m\n",
      "\u001b[32m2024-10-06 14:34:49,501 - INFO - epoch: 103 | train loss: 2.4970 | train accuracy: 99.790 | test loss: 1.5378 | test accuracy: 61.110 | epoch runtime:   5.52 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:34:55,056 - INFO - Evaluate Summary Time 1.74s\tLoss 1.5441\t Acc@1 60.8300\t Acc@5 89.8200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:34:55,057 - INFO - Head 80.400\tMid 53.050\tTail 51.633\u001b[0m\n",
      "\u001b[32m2024-10-06 14:34:55,057 - INFO - epoch: 104 | train loss: 2.4967 | train accuracy: 99.780 | test loss: 1.5441 | test accuracy: 60.830 | epoch runtime:   5.56 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:35:00,580 - INFO - Evaluate Summary Time 1.70s\tLoss 1.5329\t Acc@1 61.9400\t Acc@5 89.4900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:35:00,581 - INFO - Head 78.767\tMid 59.825\tTail 47.933\u001b[0m\n",
      "\u001b[32m2024-10-06 14:35:00,581 - INFO - epoch: 105 | train loss: 2.4953 | train accuracy: 99.785 | test loss: 1.5329 | test accuracy: 61.940 | epoch runtime:   5.52 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:35:06,131 - INFO - Evaluate Summary Time 1.74s\tLoss 1.5417\t Acc@1 61.1300\t Acc@5 89.2900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:35:06,131 - INFO - Head 78.533\tMid 55.425\tTail 51.333\u001b[0m\n",
      "\u001b[32m2024-10-06 14:35:06,132 - INFO - epoch: 106 | train loss: 2.4947 | train accuracy: 99.834 | test loss: 1.5417 | test accuracy: 61.130 | epoch runtime:   5.55 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:35:11,693 - INFO - Evaluate Summary Time 1.73s\tLoss 1.5173\t Acc@1 62.3600\t Acc@5 90.1900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:35:11,693 - INFO - Head 79.233\tMid 55.600\tTail 54.500\u001b[0m\n",
      "\u001b[32m2024-10-06 14:35:11,694 - INFO - epoch: 107 | train loss: 2.4938 | train accuracy: 99.838 | test loss: 1.5173 | test accuracy: 62.360 | epoch runtime:   5.56 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:35:17,217 - INFO - Evaluate Summary Time 1.71s\tLoss 1.5533\t Acc@1 60.3800\t Acc@5 89.3900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:35:17,217 - INFO - Head 79.600\tMid 54.900\tTail 48.467\u001b[0m\n",
      "\u001b[32m2024-10-06 14:35:17,218 - INFO - epoch: 108 | train loss: 2.4935 | train accuracy: 99.775 | test loss: 1.5533 | test accuracy: 60.380 | epoch runtime:   5.52 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:35:22,853 - INFO - Evaluate Summary Time 1.72s\tLoss 1.5418\t Acc@1 61.0900\t Acc@5 89.5300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:35:22,853 - INFO - Head 78.867\tMid 55.625\tTail 50.600\u001b[0m\n",
      "\u001b[32m2024-10-06 14:35:22,854 - INFO - epoch: 109 | train loss: 2.4931 | train accuracy: 99.809 | test loss: 1.5418 | test accuracy: 61.090 | epoch runtime:   5.64 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:35:28,423 - INFO - Evaluate Summary Time 1.63s\tLoss 1.5367\t Acc@1 61.2600\t Acc@5 89.8000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:35:28,424 - INFO - Head 79.433\tMid 55.050\tTail 51.367\u001b[0m\n",
      "\u001b[32m2024-10-06 14:35:28,424 - INFO - epoch: 110 | train loss: 2.4919 | train accuracy: 99.858 | test loss: 1.5367 | test accuracy: 61.260 | epoch runtime:   5.57 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:35:34,003 - INFO - Evaluate Summary Time 1.72s\tLoss 1.5573\t Acc@1 60.4400\t Acc@5 89.4700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:35:34,003 - INFO - Head 79.033\tMid 56.250\tTail 47.433\u001b[0m\n",
      "\u001b[32m2024-10-06 14:35:34,003 - INFO - epoch: 111 | train loss: 2.4924 | train accuracy: 99.834 | test loss: 1.5573 | test accuracy: 60.440 | epoch runtime:   5.58 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:35:39,552 - INFO - Evaluate Summary Time 1.66s\tLoss 1.5416\t Acc@1 61.0400\t Acc@5 89.4200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:35:39,552 - INFO - Head 78.133\tMid 57.150\tTail 49.133\u001b[0m\n",
      "\u001b[32m2024-10-06 14:35:39,553 - INFO - epoch: 112 | train loss: 2.4929 | train accuracy: 99.868 | test loss: 1.5416 | test accuracy: 61.040 | epoch runtime:   5.55 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:35:45,029 - INFO - Evaluate Summary Time 1.63s\tLoss 1.5430\t Acc@1 61.0600\t Acc@5 89.5400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:35:45,030 - INFO - Head 78.233\tMid 56.400\tTail 50.100\u001b[0m\n",
      "\u001b[32m2024-10-06 14:35:45,030 - INFO - epoch: 113 | train loss: 2.4919 | train accuracy: 99.834 | test loss: 1.5430 | test accuracy: 61.060 | epoch runtime:   5.48 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:35:50,598 - INFO - Evaluate Summary Time 1.71s\tLoss 1.5369\t Acc@1 60.6000\t Acc@5 89.9600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:35:50,598 - INFO - Head 80.367\tMid 54.125\tTail 49.467\u001b[0m\n",
      "\u001b[32m2024-10-06 14:35:50,599 - INFO - epoch: 114 | train loss: 2.4908 | train accuracy: 99.878 | test loss: 1.5369 | test accuracy: 60.600 | epoch runtime:   5.57 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:35:56,161 - INFO - Evaluate Summary Time 1.71s\tLoss 1.5569\t Acc@1 60.4200\t Acc@5 89.4500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:35:56,161 - INFO - Head 80.567\tMid 53.825\tTail 49.067\u001b[0m\n",
      "\u001b[32m2024-10-06 14:35:56,162 - INFO - epoch: 115 | train loss: 2.4900 | train accuracy: 99.917 | test loss: 1.5569 | test accuracy: 60.420 | epoch runtime:   5.56 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:36:01,711 - INFO - Evaluate Summary Time 1.80s\tLoss 1.5435\t Acc@1 61.0700\t Acc@5 89.6200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:36:01,711 - INFO - Head 78.833\tMid 56.050\tTail 50.000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:36:01,712 - INFO - epoch: 116 | train loss: 2.4915 | train accuracy: 99.912 | test loss: 1.5435 | test accuracy: 61.070 | epoch runtime:   5.55 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:36:07,377 - INFO - Evaluate Summary Time 1.75s\tLoss 1.5666\t Acc@1 59.7800\t Acc@5 89.2200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:36:07,378 - INFO - Head 78.667\tMid 56.025\tTail 45.900\u001b[0m\n",
      "\u001b[32m2024-10-06 14:36:07,378 - INFO - epoch: 117 | train loss: 2.4883 | train accuracy: 99.892 | test loss: 1.5666 | test accuracy: 59.780 | epoch runtime:   5.67 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:36:12,857 - INFO - Evaluate Summary Time 1.68s\tLoss 1.5328\t Acc@1 61.2000\t Acc@5 89.8900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:36:12,858 - INFO - Head 80.600\tMid 53.750\tTail 51.733\u001b[0m\n",
      "\u001b[32m2024-10-06 14:36:12,858 - INFO - epoch: 118 | train loss: 2.4892 | train accuracy: 99.917 | test loss: 1.5328 | test accuracy: 61.200 | epoch runtime:   5.48 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:36:18,536 - INFO - Evaluate Summary Time 1.72s\tLoss 1.5259\t Acc@1 61.6500\t Acc@5 89.8900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:36:18,536 - INFO - Head 78.367\tMid 58.100\tTail 49.667\u001b[0m\n",
      "\u001b[32m2024-10-06 14:36:18,536 - INFO - epoch: 119 | train loss: 2.4891 | train accuracy: 99.873 | test loss: 1.5259 | test accuracy: 61.650 | epoch runtime:   5.68 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:36:24,056 - INFO - Evaluate Summary Time 1.70s\tLoss 1.5423\t Acc@1 60.9500\t Acc@5 89.3700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:36:24,056 - INFO - Head 78.600\tMid 57.150\tTail 48.367\u001b[0m\n",
      "\u001b[32m2024-10-06 14:36:24,057 - INFO - epoch: 120 | train loss: 2.4891 | train accuracy: 99.902 | test loss: 1.5423 | test accuracy: 60.950 | epoch runtime:   5.52 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:36:29,557 - INFO - Evaluate Summary Time 1.64s\tLoss 1.5554\t Acc@1 60.3100\t Acc@5 89.4000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:36:29,557 - INFO - Head 79.700\tMid 56.825\tTail 45.567\u001b[0m\n",
      "\u001b[32m2024-10-06 14:36:29,558 - INFO - epoch: 121 | train loss: 2.4882 | train accuracy: 99.922 | test loss: 1.5554 | test accuracy: 60.310 | epoch runtime:   5.50 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:36:35,101 - INFO - Evaluate Summary Time 1.64s\tLoss 1.5335\t Acc@1 61.6400\t Acc@5 89.5400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:36:35,101 - INFO - Head 79.033\tMid 55.300\tTail 52.700\u001b[0m\n",
      "\u001b[32m2024-10-06 14:36:35,102 - INFO - epoch: 122 | train loss: 2.4886 | train accuracy: 99.887 | test loss: 1.5335 | test accuracy: 61.640 | epoch runtime:   5.54 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:36:40,713 - INFO - Evaluate Summary Time 1.71s\tLoss 1.5403\t Acc@1 61.1700\t Acc@5 89.5700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:36:40,713 - INFO - Head 79.400\tMid 55.650\tTail 50.300\u001b[0m\n",
      "\u001b[32m2024-10-06 14:36:40,714 - INFO - epoch: 123 | train loss: 2.4877 | train accuracy: 99.907 | test loss: 1.5403 | test accuracy: 61.170 | epoch runtime:   5.61 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:36:46,339 - INFO - Evaluate Summary Time 1.75s\tLoss 1.5468\t Acc@1 61.0800\t Acc@5 89.3700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:36:46,339 - INFO - Head 78.067\tMid 57.975\tTail 48.233\u001b[0m\n",
      "\u001b[32m2024-10-06 14:36:46,339 - INFO - epoch: 124 | train loss: 2.4877 | train accuracy: 99.902 | test loss: 1.5468 | test accuracy: 61.080 | epoch runtime:   5.63 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:36:51,777 - INFO - Evaluate Summary Time 1.64s\tLoss 1.5292\t Acc@1 61.6900\t Acc@5 89.9900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:36:51,777 - INFO - Head 79.467\tMid 57.000\tTail 50.167\u001b[0m\n",
      "\u001b[32m2024-10-06 14:36:51,777 - INFO - epoch: 125 | train loss: 2.4860 | train accuracy: 99.892 | test loss: 1.5292 | test accuracy: 61.690 | epoch runtime:   5.44 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:36:57,348 - INFO - Evaluate Summary Time 1.76s\tLoss 1.5374\t Acc@1 61.4700\t Acc@5 89.5500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:36:57,348 - INFO - Head 78.433\tMid 56.525\tTail 51.100\u001b[0m\n",
      "\u001b[32m2024-10-06 14:36:57,348 - INFO - epoch: 126 | train loss: 2.4873 | train accuracy: 99.922 | test loss: 1.5374 | test accuracy: 61.470 | epoch runtime:   5.57 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:37:02,957 - INFO - Evaluate Summary Time 1.74s\tLoss 1.5465\t Acc@1 60.9100\t Acc@5 89.6100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:37:02,958 - INFO - Head 78.700\tMid 56.050\tTail 49.600\u001b[0m\n",
      "\u001b[32m2024-10-06 14:37:02,958 - INFO - epoch: 127 | train loss: 2.4868 | train accuracy: 99.922 | test loss: 1.5465 | test accuracy: 60.910 | epoch runtime:   5.61 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:37:08,493 - INFO - Evaluate Summary Time 1.66s\tLoss 1.5493\t Acc@1 60.6900\t Acc@5 89.5900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:37:08,493 - INFO - Head 79.033\tMid 56.050\tTail 48.533\u001b[0m\n",
      "\u001b[32m2024-10-06 14:37:08,493 - INFO - epoch: 128 | train loss: 2.4869 | train accuracy: 99.922 | test loss: 1.5493 | test accuracy: 60.690 | epoch runtime:   5.53 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:37:13,977 - INFO - Evaluate Summary Time 1.68s\tLoss 1.5396\t Acc@1 61.2000\t Acc@5 89.5300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:37:13,978 - INFO - Head 79.333\tMid 58.225\tTail 47.033\u001b[0m\n",
      "\u001b[32m2024-10-06 14:37:13,978 - INFO - epoch: 129 | train loss: 2.4864 | train accuracy: 99.897 | test loss: 1.5396 | test accuracy: 61.200 | epoch runtime:   5.48 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:37:19,603 - INFO - Evaluate Summary Time 1.73s\tLoss 1.5567\t Acc@1 60.2500\t Acc@5 89.3700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:37:19,603 - INFO - Head 78.567\tMid 56.150\tTail 47.400\u001b[0m\n",
      "\u001b[32m2024-10-06 14:37:19,603 - INFO - epoch: 130 | train loss: 2.4848 | train accuracy: 99.917 | test loss: 1.5567 | test accuracy: 60.250 | epoch runtime:   5.63 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:37:25,140 - INFO - Evaluate Summary Time 1.66s\tLoss 1.5609\t Acc@1 60.2000\t Acc@5 89.1800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:37:25,141 - INFO - Head 79.767\tMid 53.225\tTail 49.933\u001b[0m\n",
      "\u001b[32m2024-10-06 14:37:25,141 - INFO - epoch: 131 | train loss: 2.4856 | train accuracy: 99.927 | test loss: 1.5609 | test accuracy: 60.200 | epoch runtime:   5.54 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:37:30,773 - INFO - Evaluate Summary Time 1.74s\tLoss 1.5370\t Acc@1 60.9800\t Acc@5 89.6700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:37:30,773 - INFO - Head 78.767\tMid 55.500\tTail 50.500\u001b[0m\n",
      "\u001b[32m2024-10-06 14:37:30,774 - INFO - epoch: 132 | train loss: 2.4851 | train accuracy: 99.941 | test loss: 1.5370 | test accuracy: 60.980 | epoch runtime:   5.63 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:37:36,399 - INFO - Evaluate Summary Time 1.77s\tLoss 1.5331\t Acc@1 61.3000\t Acc@5 89.9200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:37:36,400 - INFO - Head 79.367\tMid 56.700\tTail 49.367\u001b[0m\n",
      "\u001b[32m2024-10-06 14:37:36,400 - INFO - epoch: 133 | train loss: 2.4858 | train accuracy: 99.931 | test loss: 1.5331 | test accuracy: 61.300 | epoch runtime:   5.63 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:37:41,883 - INFO - Evaluate Summary Time 1.73s\tLoss 1.5408\t Acc@1 60.8700\t Acc@5 89.5900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:37:41,883 - INFO - Head 78.100\tMid 56.225\tTail 49.833\u001b[0m\n",
      "\u001b[32m2024-10-06 14:37:41,884 - INFO - epoch: 134 | train loss: 2.4857 | train accuracy: 99.917 | test loss: 1.5408 | test accuracy: 60.870 | epoch runtime:   5.48 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:37:47,512 - INFO - Evaluate Summary Time 1.76s\tLoss 1.5383\t Acc@1 61.0100\t Acc@5 89.4600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:37:47,513 - INFO - Head 79.333\tMid 55.450\tTail 50.100\u001b[0m\n",
      "\u001b[32m2024-10-06 14:37:47,513 - INFO - epoch: 135 | train loss: 2.4859 | train accuracy: 99.931 | test loss: 1.5383 | test accuracy: 61.010 | epoch runtime:   5.63 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:37:53,165 - INFO - Evaluate Summary Time 1.80s\tLoss 1.5435\t Acc@1 60.8100\t Acc@5 89.5800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:37:53,165 - INFO - Head 78.867\tMid 56.050\tTail 49.100\u001b[0m\n",
      "\u001b[32m2024-10-06 14:37:53,166 - INFO - epoch: 136 | train loss: 2.4850 | train accuracy: 99.936 | test loss: 1.5435 | test accuracy: 60.810 | epoch runtime:   5.65 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:37:58,764 - INFO - Evaluate Summary Time 1.73s\tLoss 1.5364\t Acc@1 61.1800\t Acc@5 89.5800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:37:58,765 - INFO - Head 78.700\tMid 56.200\tTail 50.300\u001b[0m\n",
      "\u001b[32m2024-10-06 14:37:58,765 - INFO - epoch: 137 | train loss: 2.4864 | train accuracy: 99.931 | test loss: 1.5364 | test accuracy: 61.180 | epoch runtime:   5.60 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:38:04,246 - INFO - Evaluate Summary Time 1.66s\tLoss 1.5444\t Acc@1 60.9300\t Acc@5 89.7500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:38:04,246 - INFO - Head 79.033\tMid 55.525\tTail 50.033\u001b[0m\n",
      "\u001b[32m2024-10-06 14:38:04,246 - INFO - epoch: 138 | train loss: 2.4857 | train accuracy: 99.927 | test loss: 1.5444 | test accuracy: 60.930 | epoch runtime:   5.48 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:38:09,895 - INFO - Evaluate Summary Time 1.80s\tLoss 1.5604\t Acc@1 60.3300\t Acc@5 89.2300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:38:09,895 - INFO - Head 78.033\tMid 55.325\tTail 49.300\u001b[0m\n",
      "\u001b[32m2024-10-06 14:38:09,896 - INFO - epoch: 139 | train loss: 2.4863 | train accuracy: 99.931 | test loss: 1.5604 | test accuracy: 60.330 | epoch runtime:   5.65 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:38:15,551 - INFO - Evaluate Summary Time 1.78s\tLoss 1.5462\t Acc@1 61.0300\t Acc@5 89.5700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:38:15,552 - INFO - Head 78.467\tMid 57.175\tTail 48.733\u001b[0m\n",
      "\u001b[32m2024-10-06 14:38:15,552 - INFO - epoch: 140 | train loss: 2.4842 | train accuracy: 99.946 | test loss: 1.5462 | test accuracy: 61.030 | epoch runtime:   5.66 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:38:21,121 - INFO - Evaluate Summary Time 1.80s\tLoss 1.5525\t Acc@1 60.8700\t Acc@5 89.5400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:38:21,121 - INFO - Head 78.367\tMid 56.275\tTail 49.500\u001b[0m\n",
      "\u001b[32m2024-10-06 14:38:21,121 - INFO - epoch: 141 | train loss: 2.4845 | train accuracy: 99.936 | test loss: 1.5525 | test accuracy: 60.870 | epoch runtime:   5.57 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:38:26,577 - INFO - Evaluate Summary Time 1.66s\tLoss 1.5546\t Acc@1 60.5300\t Acc@5 89.5700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:38:26,577 - INFO - Head 79.733\tMid 55.600\tTail 47.900\u001b[0m\n",
      "\u001b[32m2024-10-06 14:38:26,578 - INFO - epoch: 142 | train loss: 2.4841 | train accuracy: 99.931 | test loss: 1.5546 | test accuracy: 60.530 | epoch runtime:   5.46 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:38:32,090 - INFO - Evaluate Summary Time 1.67s\tLoss 1.5503\t Acc@1 60.7300\t Acc@5 89.4800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:38:32,091 - INFO - Head 79.467\tMid 56.650\tTail 47.433\u001b[0m\n",
      "\u001b[32m2024-10-06 14:38:32,091 - INFO - epoch: 143 | train loss: 2.4837 | train accuracy: 99.936 | test loss: 1.5503 | test accuracy: 60.730 | epoch runtime:   5.51 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:38:37,688 - INFO - Evaluate Summary Time 1.70s\tLoss 1.5579\t Acc@1 59.7100\t Acc@5 89.3000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:38:37,689 - INFO - Head 80.633\tMid 54.000\tTail 46.400\u001b[0m\n",
      "\u001b[32m2024-10-06 14:38:37,689 - INFO - epoch: 144 | train loss: 2.4843 | train accuracy: 99.936 | test loss: 1.5579 | test accuracy: 59.710 | epoch runtime:   5.60 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:38:43,266 - INFO - Evaluate Summary Time 1.71s\tLoss 1.5416\t Acc@1 61.0600\t Acc@5 89.8300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:38:43,267 - INFO - Head 80.633\tMid 55.050\tTail 49.500\u001b[0m\n",
      "\u001b[32m2024-10-06 14:38:43,267 - INFO - epoch: 145 | train loss: 2.4836 | train accuracy: 99.927 | test loss: 1.5416 | test accuracy: 61.060 | epoch runtime:   5.58 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:38:48,866 - INFO - Evaluate Summary Time 1.77s\tLoss 1.5514\t Acc@1 60.9800\t Acc@5 89.5200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:38:48,867 - INFO - Head 78.633\tMid 55.375\tTail 50.800\u001b[0m\n",
      "\u001b[32m2024-10-06 14:38:48,867 - INFO - epoch: 146 | train loss: 2.4843 | train accuracy: 99.941 | test loss: 1.5514 | test accuracy: 60.980 | epoch runtime:   5.60 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:38:54,377 - INFO - Evaluate Summary Time 1.64s\tLoss 1.5515\t Acc@1 60.8700\t Acc@5 89.7400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:38:54,377 - INFO - Head 78.633\tMid 56.675\tTail 48.700\u001b[0m\n",
      "\u001b[32m2024-10-06 14:38:54,377 - INFO - epoch: 147 | train loss: 2.4840 | train accuracy: 99.907 | test loss: 1.5515 | test accuracy: 60.870 | epoch runtime:   5.51 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:38:59,927 - INFO - Evaluate Summary Time 1.64s\tLoss 1.5380\t Acc@1 61.6800\t Acc@5 89.7400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:38:59,927 - INFO - Head 78.900\tMid 57.100\tTail 50.567\u001b[0m\n",
      "\u001b[32m2024-10-06 14:38:59,928 - INFO - epoch: 148 | train loss: 2.4839 | train accuracy: 99.907 | test loss: 1.5380 | test accuracy: 61.680 | epoch runtime:   5.55 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:39:05,538 - INFO - Evaluate Summary Time 1.75s\tLoss 1.5449\t Acc@1 61.3000\t Acc@5 89.6800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:39:05,539 - INFO - Head 78.267\tMid 55.525\tTail 52.033\u001b[0m\n",
      "\u001b[32m2024-10-06 14:39:05,539 - INFO - epoch: 149 | train loss: 2.4841 | train accuracy: 99.936 | test loss: 1.5449 | test accuracy: 61.300 | epoch runtime:   5.61 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 14:39:11,220 - INFO - Evaluate Summary Time 1.73s\tLoss 1.5532\t Acc@1 60.8600\t Acc@5 89.5700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:39:11,220 - INFO - Head 79.633\tMid 56.125\tTail 48.400\u001b[0m\n",
      "\u001b[32m2024-10-06 14:39:11,221 - INFO - epoch: 150 | train loss: 2.4841 | train accuracy: 99.951 | test loss: 1.5532 | test accuracy: 60.860 | epoch runtime:   5.68 sec | best accuracy: 66.560 @ epoch: 030\u001b[0m\n",
      "Runtime of this script /home/zyx/zhengjinpeng/PNP/cifar.py : 836.9 seconds (0.232 hours)\n",
      "Config:\n",
      "{\n",
      "    database: Datasets\n",
      "    dataset: cifar10\n",
      "    n_classes: 10\n",
      "    rescale_size: 32\n",
      "    crop_size: 32\n",
      "    cfg_file: ./config/cifar10.cfg\n",
      "    synthetic_data: cifar80no\n",
      "    noise_type: asymmetric\n",
      "    closeset_ratio: 0.2\n",
      "    r_ood: 0.2\n",
      "    r_imb: 0.1\n",
      "    gpu: 0\n",
      "    net: cnn\n",
      "    batch_size: 128\n",
      "    lr: 0.001\n",
      "    lr_decay: cosine\n",
      "    weight_decay: 1e-05\n",
      "    opt: adam\n",
      "    warmup_epochs: 5\n",
      "    warmup_lr_scale: 10.0\n",
      "    epochs: 150\n",
      "    save_model: False\n",
      "    use_fp16: False\n",
      "    use_grad_accumulate: False\n",
      "    project: \n",
      "    log: PENIOC\n",
      "    epsilon: 0.5\n",
      "    temperature: 0.1\n",
      "    eta: 0.5\n",
      "    alpha: 0.0\n",
      "    beta: 1.0\n",
      "    gamma: 1.0\n",
      "    omega: 0.1\n",
      "    rho: 1.0\n",
      "    loss_func_aux: mae\n",
      "    weighting: soft\n",
      "    neg_cons: False\n",
      "    activation: tanh\n",
      "    ablation: False\n",
      "    log_freq: 1\n",
      "    asym: True\n",
      "}\n",
      "\n",
      "Available GPUs Index : 0\n",
      "using CIFAR-10...\n",
      "Built imbalanced dataset, r_imb=0.1\n",
      "Mixing in OOD noise, r_ood=0.2\n",
      "Mixing in ID asym noise, r_id=0.2\n",
      "using CIFAR-10...\n",
      "\u001b[32m2024-10-06 14:39:18,556 - INFO - Categories: 10, Training Samples: 20431, Testing Samples: 10000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:39:18,556 - INFO - Optimizer: adam\u001b[0m\n",
      "\u001b[32m2024-10-06 14:39:18,556 - INFO - Accumulate gradients every 1 iterations --> Acutal batch size is 128\u001b[0m\n",
      "\u001b[32m2024-10-06 14:39:24,296 - INFO - Evaluate Summary Time 1.66s\tLoss 2.0956\t Acc@1 25.1200\t Acc@5 77.8400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:39:24,296 - INFO - Head 74.300\tMid 7.025\tTail 0.067\u001b[0m\n",
      "\u001b[32m2024-10-06 14:39:24,297 - INFO - epoch:   1 | train loss: 2.2556 | train accuracy: 34.604 | test loss: 2.0956 | test accuracy: 25.120 | epoch runtime:   5.74 sec | best accuracy: 25.120 @ epoch: 001\u001b[0m\n",
      "\u001b[32m2024-10-06 14:39:29,199 - INFO - Evaluate Summary Time 1.67s\tLoss 1.8867\t Acc@1 38.0400\t Acc@5 86.7800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:39:29,200 - INFO - Head 68.100\tMid 34.250\tTail 13.033\u001b[0m\n",
      "\u001b[32m2024-10-06 14:39:29,200 - INFO - epoch:   2 | train loss: 2.1785 | train accuracy: 43.522 | test loss: 1.8867 | test accuracy: 38.040 | epoch runtime:   4.90 sec | best accuracy: 38.040 @ epoch: 002\u001b[0m\n",
      "\u001b[32m2024-10-06 14:39:34,133 - INFO - Evaluate Summary Time 1.65s\tLoss 1.8924\t Acc@1 38.0600\t Acc@5 85.7200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:39:34,134 - INFO - Head 74.967\tMid 24.600\tTail 19.100\u001b[0m\n",
      "\u001b[32m2024-10-06 14:39:34,134 - INFO - epoch:   3 | train loss: 2.1582 | train accuracy: 47.159 | test loss: 1.8924 | test accuracy: 38.060 | epoch runtime:   4.93 sec | best accuracy: 38.060 @ epoch: 003\u001b[0m\n",
      "\u001b[32m2024-10-06 14:39:39,098 - INFO - Evaluate Summary Time 1.67s\tLoss 1.6972\t Acc@1 49.8400\t Acc@5 91.9500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:39:39,099 - INFO - Head 75.067\tMid 45.375\tTail 30.567\u001b[0m\n",
      "\u001b[32m2024-10-06 14:39:39,099 - INFO - epoch:   4 | train loss: 2.1235 | train accuracy: 50.389 | test loss: 1.6972 | test accuracy: 49.840 | epoch runtime:   4.96 sec | best accuracy: 49.840 @ epoch: 004\u001b[0m\n",
      "\u001b[32m2024-10-06 14:39:44,060 - INFO - Evaluate Summary Time 1.68s\tLoss 1.6473\t Acc@1 49.3600\t Acc@5 93.5900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:39:44,061 - INFO - Head 75.133\tMid 50.700\tTail 21.800\u001b[0m\n",
      "\u001b[32m2024-10-06 14:39:44,061 - INFO - epoch:   5 | train loss: 2.1023 | train accuracy: 53.840 | test loss: 1.6473 | test accuracy: 49.360 | epoch runtime:   4.96 sec | best accuracy: 49.840 @ epoch: 004\u001b[0m\n",
      "\u001b[32m2024-10-06 14:39:49,629 - INFO - Evaluate Summary Time 1.67s\tLoss 1.6271\t Acc@1 55.1700\t Acc@5 94.3200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:39:49,630 - INFO - Head 80.700\tMid 49.800\tTail 36.800\u001b[0m\n",
      "\u001b[32m2024-10-06 14:39:49,630 - INFO - epoch:   6 | train loss: 2.6923 | train accuracy: 57.604 | test loss: 1.6271 | test accuracy: 55.170 | epoch runtime:   5.57 sec | best accuracy: 55.170 @ epoch: 006\u001b[0m\n",
      "\u001b[32m2024-10-06 14:39:55,220 - INFO - Evaluate Summary Time 1.72s\tLoss 1.6221\t Acc@1 55.6200\t Acc@5 94.1300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:39:55,220 - INFO - Head 82.133\tMid 50.175\tTail 36.367\u001b[0m\n",
      "\u001b[32m2024-10-06 14:39:55,220 - INFO - epoch:   7 | train loss: 2.6649 | train accuracy: 58.583 | test loss: 1.6221 | test accuracy: 55.620 | epoch runtime:   5.59 sec | best accuracy: 55.620 @ epoch: 007\u001b[0m\n",
      "\u001b[32m2024-10-06 14:40:00,787 - INFO - Evaluate Summary Time 1.70s\tLoss 1.6133\t Acc@1 55.8200\t Acc@5 94.2200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:40:00,787 - INFO - Head 80.667\tMid 52.375\tTail 35.567\u001b[0m\n",
      "\u001b[32m2024-10-06 14:40:00,787 - INFO - epoch:   8 | train loss: 2.6582 | train accuracy: 59.493 | test loss: 1.6133 | test accuracy: 55.820 | epoch runtime:   5.57 sec | best accuracy: 55.820 @ epoch: 008\u001b[0m\n",
      "\u001b[32m2024-10-06 14:40:06,243 - INFO - Evaluate Summary Time 1.68s\tLoss 1.5519\t Acc@1 59.9900\t Acc@5 95.1700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:40:06,244 - INFO - Head 83.300\tMid 54.800\tTail 43.600\u001b[0m\n",
      "\u001b[32m2024-10-06 14:40:06,244 - INFO - epoch:   9 | train loss: 2.6532 | train accuracy: 59.884 | test loss: 1.5519 | test accuracy: 59.990 | epoch runtime:   5.46 sec | best accuracy: 59.990 @ epoch: 009\u001b[0m\n",
      "\u001b[32m2024-10-06 14:40:11,740 - INFO - Evaluate Summary Time 1.72s\tLoss 1.5349\t Acc@1 60.4700\t Acc@5 95.4600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:40:11,741 - INFO - Head 83.133\tMid 56.075\tTail 43.667\u001b[0m\n",
      "\u001b[32m2024-10-06 14:40:11,741 - INFO - epoch:  10 | train loss: 2.6499 | train accuracy: 60.369 | test loss: 1.5349 | test accuracy: 60.470 | epoch runtime:   5.50 sec | best accuracy: 60.470 @ epoch: 010\u001b[0m\n",
      "\u001b[32m2024-10-06 14:40:17,275 - INFO - Evaluate Summary Time 1.64s\tLoss 1.5584\t Acc@1 60.3900\t Acc@5 94.7900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:40:17,276 - INFO - Head 82.333\tMid 54.850\tTail 45.833\u001b[0m\n",
      "\u001b[32m2024-10-06 14:40:17,276 - INFO - epoch:  11 | train loss: 2.6449 | train accuracy: 61.074 | test loss: 1.5584 | test accuracy: 60.390 | epoch runtime:   5.54 sec | best accuracy: 60.470 @ epoch: 010\u001b[0m\n",
      "\u001b[32m2024-10-06 14:40:22,895 - INFO - Evaluate Summary Time 1.77s\tLoss 1.4893\t Acc@1 63.9500\t Acc@5 95.4900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:40:22,895 - INFO - Head 82.467\tMid 58.375\tTail 52.867\u001b[0m\n",
      "\u001b[32m2024-10-06 14:40:22,895 - INFO - epoch:  12 | train loss: 2.6406 | train accuracy: 61.818 | test loss: 1.4893 | test accuracy: 63.950 | epoch runtime:   5.62 sec | best accuracy: 63.950 @ epoch: 012\u001b[0m\n",
      "\u001b[32m2024-10-06 14:40:28,475 - INFO - Evaluate Summary Time 1.75s\tLoss 1.4660\t Acc@1 63.3100\t Acc@5 95.6200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:40:28,476 - INFO - Head 82.900\tMid 56.150\tTail 53.267\u001b[0m\n",
      "\u001b[32m2024-10-06 14:40:28,476 - INFO - epoch:  13 | train loss: 2.6317 | train accuracy: 62.097 | test loss: 1.4660 | test accuracy: 63.310 | epoch runtime:   5.58 sec | best accuracy: 63.950 @ epoch: 012\u001b[0m\n",
      "\u001b[32m2024-10-06 14:40:33,990 - INFO - Evaluate Summary Time 1.71s\tLoss 1.4884\t Acc@1 62.7300\t Acc@5 95.4900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:40:33,990 - INFO - Head 85.333\tMid 55.625\tTail 49.600\u001b[0m\n",
      "\u001b[32m2024-10-06 14:40:33,990 - INFO - epoch:  14 | train loss: 2.6249 | train accuracy: 62.518 | test loss: 1.4884 | test accuracy: 62.730 | epoch runtime:   5.51 sec | best accuracy: 63.950 @ epoch: 012\u001b[0m\n",
      "\u001b[32m2024-10-06 14:40:39,532 - INFO - Evaluate Summary Time 1.68s\tLoss 1.3900\t Acc@1 66.6200\t Acc@5 96.1000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:40:39,533 - INFO - Head 85.467\tMid 59.700\tTail 57.000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:40:39,533 - INFO - epoch:  15 | train loss: 2.6205 | train accuracy: 63.350 | test loss: 1.3900 | test accuracy: 66.620 | epoch runtime:   5.54 sec | best accuracy: 66.620 @ epoch: 015\u001b[0m\n",
      "\u001b[32m2024-10-06 14:40:45,005 - INFO - Evaluate Summary Time 1.68s\tLoss 1.4235\t Acc@1 65.6200\t Acc@5 95.9900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:40:45,005 - INFO - Head 83.600\tMid 59.075\tTail 56.367\u001b[0m\n",
      "\u001b[32m2024-10-06 14:40:45,006 - INFO - epoch:  16 | train loss: 2.6164 | train accuracy: 64.226 | test loss: 1.4235 | test accuracy: 65.620 | epoch runtime:   5.47 sec | best accuracy: 66.620 @ epoch: 015\u001b[0m\n",
      "\u001b[32m2024-10-06 14:40:50,654 - INFO - Evaluate Summary Time 1.79s\tLoss 1.4203\t Acc@1 67.2900\t Acc@5 96.0600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:40:50,655 - INFO - Head 85.467\tMid 59.200\tTail 59.900\u001b[0m\n",
      "\u001b[32m2024-10-06 14:40:50,655 - INFO - epoch:  17 | train loss: 2.6130 | train accuracy: 65.371 | test loss: 1.4203 | test accuracy: 67.290 | epoch runtime:   5.65 sec | best accuracy: 67.290 @ epoch: 017\u001b[0m\n",
      "\u001b[32m2024-10-06 14:40:56,096 - INFO - Evaluate Summary Time 1.64s\tLoss 1.3650\t Acc@1 67.4900\t Acc@5 96.0800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:40:56,096 - INFO - Head 82.333\tMid 63.600\tTail 57.833\u001b[0m\n",
      "\u001b[32m2024-10-06 14:40:56,097 - INFO - epoch:  18 | train loss: 2.6099 | train accuracy: 65.733 | test loss: 1.3650 | test accuracy: 67.490 | epoch runtime:   5.44 sec | best accuracy: 67.490 @ epoch: 018\u001b[0m\n",
      "\u001b[32m2024-10-06 14:41:01,696 - INFO - Evaluate Summary Time 1.73s\tLoss 1.3827\t Acc@1 65.8300\t Acc@5 96.4000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:41:01,697 - INFO - Head 85.300\tMid 60.225\tTail 53.833\u001b[0m\n",
      "\u001b[32m2024-10-06 14:41:01,697 - INFO - epoch:  19 | train loss: 2.6036 | train accuracy: 66.492 | test loss: 1.3827 | test accuracy: 65.830 | epoch runtime:   5.60 sec | best accuracy: 67.490 @ epoch: 018\u001b[0m\n",
      "\u001b[32m2024-10-06 14:41:07,334 - INFO - Evaluate Summary Time 1.73s\tLoss 1.3869\t Acc@1 65.5200\t Acc@5 96.0800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:41:07,334 - INFO - Head 82.067\tMid 62.050\tTail 53.600\u001b[0m\n",
      "\u001b[32m2024-10-06 14:41:07,334 - INFO - epoch:  20 | train loss: 2.6006 | train accuracy: 67.569 | test loss: 1.3869 | test accuracy: 65.520 | epoch runtime:   5.64 sec | best accuracy: 67.490 @ epoch: 018\u001b[0m\n",
      "\u001b[32m2024-10-06 14:41:12,886 - INFO - Evaluate Summary Time 1.73s\tLoss 1.4025\t Acc@1 65.3200\t Acc@5 96.0600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:41:12,887 - INFO - Head 86.067\tMid 59.575\tTail 52.233\u001b[0m\n",
      "\u001b[32m2024-10-06 14:41:12,887 - INFO - epoch:  21 | train loss: 2.5968 | train accuracy: 67.926 | test loss: 1.4025 | test accuracy: 65.320 | epoch runtime:   5.55 sec | best accuracy: 67.490 @ epoch: 018\u001b[0m\n",
      "\u001b[32m2024-10-06 14:41:18,404 - INFO - Evaluate Summary Time 1.67s\tLoss 1.3689\t Acc@1 67.7000\t Acc@5 96.4000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:41:18,404 - INFO - Head 83.233\tMid 65.400\tTail 55.233\u001b[0m\n",
      "\u001b[32m2024-10-06 14:41:18,404 - INFO - epoch:  22 | train loss: 2.5938 | train accuracy: 68.611 | test loss: 1.3689 | test accuracy: 67.700 | epoch runtime:   5.52 sec | best accuracy: 67.700 @ epoch: 022\u001b[0m\n",
      "\u001b[32m2024-10-06 14:41:23,905 - INFO - Evaluate Summary Time 1.67s\tLoss 1.3408\t Acc@1 68.2100\t Acc@5 96.4100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:41:23,905 - INFO - Head 83.733\tMid 61.950\tTail 61.033\u001b[0m\n",
      "\u001b[32m2024-10-06 14:41:23,905 - INFO - epoch:  23 | train loss: 2.5894 | train accuracy: 69.595 | test loss: 1.3408 | test accuracy: 68.210 | epoch runtime:   5.50 sec | best accuracy: 68.210 @ epoch: 023\u001b[0m\n",
      "\u001b[32m2024-10-06 14:41:29,408 - INFO - Evaluate Summary Time 1.65s\tLoss 1.2943\t Acc@1 70.9200\t Acc@5 96.5800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:41:29,408 - INFO - Head 83.233\tMid 66.850\tTail 64.033\u001b[0m\n",
      "\u001b[32m2024-10-06 14:41:29,408 - INFO - epoch:  24 | train loss: 2.5862 | train accuracy: 70.892 | test loss: 1.2943 | test accuracy: 70.920 | epoch runtime:   5.50 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:41:34,958 - INFO - Evaluate Summary Time 1.72s\tLoss 1.3165\t Acc@1 68.6600\t Acc@5 96.6200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:41:34,958 - INFO - Head 83.367\tMid 61.075\tTail 64.067\u001b[0m\n",
      "\u001b[32m2024-10-06 14:41:34,958 - INFO - epoch:  25 | train loss: 2.5830 | train accuracy: 71.989 | test loss: 1.3165 | test accuracy: 68.660 | epoch runtime:   5.55 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:41:40,537 - INFO - Evaluate Summary Time 1.74s\tLoss 1.2995\t Acc@1 69.6100\t Acc@5 96.6300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:41:40,538 - INFO - Head 85.833\tMid 63.350\tTail 61.733\u001b[0m\n",
      "\u001b[32m2024-10-06 14:41:40,538 - INFO - epoch:  26 | train loss: 2.5796 | train accuracy: 72.904 | test loss: 1.2995 | test accuracy: 69.610 | epoch runtime:   5.58 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:41:46,046 - INFO - Evaluate Summary Time 1.74s\tLoss 1.3552\t Acc@1 66.7300\t Acc@5 96.1600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:41:46,047 - INFO - Head 85.900\tMid 62.700\tTail 52.933\u001b[0m\n",
      "\u001b[32m2024-10-06 14:41:46,047 - INFO - epoch:  27 | train loss: 2.5757 | train accuracy: 74.074 | test loss: 1.3552 | test accuracy: 66.730 | epoch runtime:   5.51 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:41:51,555 - INFO - Evaluate Summary Time 1.71s\tLoss 1.3008\t Acc@1 70.3700\t Acc@5 96.1900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:41:51,556 - INFO - Head 84.067\tMid 66.325\tTail 62.067\u001b[0m\n",
      "\u001b[32m2024-10-06 14:41:51,556 - INFO - epoch:  28 | train loss: 2.5728 | train accuracy: 75.077 | test loss: 1.3008 | test accuracy: 70.370 | epoch runtime:   5.51 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:41:57,099 - INFO - Evaluate Summary Time 1.73s\tLoss 1.2673\t Acc@1 68.9600\t Acc@5 96.2500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:41:57,100 - INFO - Head 80.533\tMid 63.900\tTail 64.133\u001b[0m\n",
      "\u001b[32m2024-10-06 14:41:57,100 - INFO - epoch:  29 | train loss: 2.5677 | train accuracy: 75.992 | test loss: 1.2673 | test accuracy: 68.960 | epoch runtime:   5.54 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:42:02,596 - INFO - Evaluate Summary Time 1.70s\tLoss 1.3050\t Acc@1 68.1700\t Acc@5 95.7000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:42:02,597 - INFO - Head 87.067\tMid 62.575\tTail 56.733\u001b[0m\n",
      "\u001b[32m2024-10-06 14:42:02,597 - INFO - epoch:  30 | train loss: 2.5645 | train accuracy: 77.353 | test loss: 1.3050 | test accuracy: 68.170 | epoch runtime:   5.50 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:42:08,234 - INFO - Evaluate Summary Time 1.72s\tLoss 1.2608\t Acc@1 70.4500\t Acc@5 96.3400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:42:08,235 - INFO - Head 84.767\tMid 64.025\tTail 64.700\u001b[0m\n",
      "\u001b[32m2024-10-06 14:42:08,235 - INFO - epoch:  31 | train loss: 2.5599 | train accuracy: 78.097 | test loss: 1.2608 | test accuracy: 70.450 | epoch runtime:   5.64 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:42:13,696 - INFO - Evaluate Summary Time 1.73s\tLoss 1.3046\t Acc@1 68.7700\t Acc@5 96.0600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:42:13,697 - INFO - Head 85.133\tMid 64.600\tTail 57.967\u001b[0m\n",
      "\u001b[32m2024-10-06 14:42:13,697 - INFO - epoch:  32 | train loss: 2.5561 | train accuracy: 79.321 | test loss: 1.3046 | test accuracy: 68.770 | epoch runtime:   5.46 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:42:19,152 - INFO - Evaluate Summary Time 1.66s\tLoss 1.3379\t Acc@1 64.4500\t Acc@5 95.5100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:42:19,152 - INFO - Head 84.800\tMid 56.950\tTail 54.100\u001b[0m\n",
      "\u001b[32m2024-10-06 14:42:19,152 - INFO - epoch:  33 | train loss: 2.5533 | train accuracy: 80.207 | test loss: 1.3379 | test accuracy: 64.450 | epoch runtime:   5.45 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:42:24,589 - INFO - Evaluate Summary Time 1.66s\tLoss 1.2978\t Acc@1 69.1900\t Acc@5 95.7600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:42:24,590 - INFO - Head 84.133\tMid 63.750\tTail 61.500\u001b[0m\n",
      "\u001b[32m2024-10-06 14:42:24,590 - INFO - epoch:  34 | train loss: 2.5478 | train accuracy: 81.391 | test loss: 1.2978 | test accuracy: 69.190 | epoch runtime:   5.44 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:42:30,198 - INFO - Evaluate Summary Time 1.70s\tLoss 1.3182\t Acc@1 67.7000\t Acc@5 95.4800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:42:30,199 - INFO - Head 82.900\tMid 62.800\tTail 59.033\u001b[0m\n",
      "\u001b[32m2024-10-06 14:42:30,199 - INFO - epoch:  35 | train loss: 2.5438 | train accuracy: 82.903 | test loss: 1.3182 | test accuracy: 67.700 | epoch runtime:   5.61 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:42:35,725 - INFO - Evaluate Summary Time 1.69s\tLoss 1.2794\t Acc@1 69.8400\t Acc@5 95.7900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:42:35,725 - INFO - Head 80.100\tMid 64.200\tTail 67.100\u001b[0m\n",
      "\u001b[32m2024-10-06 14:42:35,726 - INFO - epoch:  36 | train loss: 2.5402 | train accuracy: 83.285 | test loss: 1.2794 | test accuracy: 69.840 | epoch runtime:   5.53 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:42:41,141 - INFO - Evaluate Summary Time 1.56s\tLoss 1.3413\t Acc@1 66.4200\t Acc@5 94.9200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:42:41,142 - INFO - Head 83.967\tMid 60.600\tTail 56.633\u001b[0m\n",
      "\u001b[32m2024-10-06 14:42:41,142 - INFO - epoch:  37 | train loss: 2.5378 | train accuracy: 84.249 | test loss: 1.3413 | test accuracy: 66.420 | epoch runtime:   5.42 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:42:46,735 - INFO - Evaluate Summary Time 1.78s\tLoss 1.3008\t Acc@1 67.8400\t Acc@5 95.3800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:42:46,736 - INFO - Head 81.633\tMid 61.800\tTail 62.100\u001b[0m\n",
      "\u001b[32m2024-10-06 14:42:46,736 - INFO - epoch:  38 | train loss: 2.5336 | train accuracy: 85.346 | test loss: 1.3008 | test accuracy: 67.840 | epoch runtime:   5.59 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:42:52,227 - INFO - Evaluate Summary Time 1.69s\tLoss 1.3254\t Acc@1 67.1900\t Acc@5 95.0100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:42:52,228 - INFO - Head 82.400\tMid 63.225\tTail 57.267\u001b[0m\n",
      "\u001b[32m2024-10-06 14:42:52,228 - INFO - epoch:  39 | train loss: 2.5291 | train accuracy: 86.261 | test loss: 1.3254 | test accuracy: 67.190 | epoch runtime:   5.49 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:42:57,790 - INFO - Evaluate Summary Time 1.69s\tLoss 1.3028\t Acc@1 67.7300\t Acc@5 95.2200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:42:57,790 - INFO - Head 83.933\tMid 58.250\tTail 64.167\u001b[0m\n",
      "\u001b[32m2024-10-06 14:42:57,791 - INFO - epoch:  40 | train loss: 2.5274 | train accuracy: 87.137 | test loss: 1.3028 | test accuracy: 67.730 | epoch runtime:   5.56 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:43:03,357 - INFO - Evaluate Summary Time 1.73s\tLoss 1.3110\t Acc@1 66.8500\t Acc@5 95.5200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:43:03,358 - INFO - Head 81.067\tMid 60.750\tTail 60.767\u001b[0m\n",
      "\u001b[32m2024-10-06 14:43:03,358 - INFO - epoch:  41 | train loss: 2.5236 | train accuracy: 87.725 | test loss: 1.3110 | test accuracy: 66.850 | epoch runtime:   5.57 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:43:08,929 - INFO - Evaluate Summary Time 1.73s\tLoss 1.3216\t Acc@1 66.7800\t Acc@5 94.8300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:43:08,930 - INFO - Head 83.533\tMid 58.325\tTail 61.300\u001b[0m\n",
      "\u001b[32m2024-10-06 14:43:08,930 - INFO - epoch:  42 | train loss: 2.5216 | train accuracy: 88.552 | test loss: 1.3216 | test accuracy: 66.780 | epoch runtime:   5.57 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:43:14,584 - INFO - Evaluate Summary Time 1.79s\tLoss 1.3086\t Acc@1 66.8600\t Acc@5 96.0200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:43:14,584 - INFO - Head 81.733\tMid 59.925\tTail 61.233\u001b[0m\n",
      "\u001b[32m2024-10-06 14:43:14,585 - INFO - epoch:  43 | train loss: 2.5174 | train accuracy: 89.075 | test loss: 1.3086 | test accuracy: 66.860 | epoch runtime:   5.65 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:43:20,107 - INFO - Evaluate Summary Time 1.69s\tLoss 1.3230\t Acc@1 66.4200\t Acc@5 95.0600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:43:20,107 - INFO - Head 82.333\tMid 61.325\tTail 57.300\u001b[0m\n",
      "\u001b[32m2024-10-06 14:43:20,107 - INFO - epoch:  44 | train loss: 2.5145 | train accuracy: 89.932 | test loss: 1.3230 | test accuracy: 66.420 | epoch runtime:   5.52 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:43:25,710 - INFO - Evaluate Summary Time 1.70s\tLoss 1.2841\t Acc@1 68.7700\t Acc@5 95.5000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:43:25,711 - INFO - Head 83.967\tMid 62.475\tTail 61.967\u001b[0m\n",
      "\u001b[32m2024-10-06 14:43:25,711 - INFO - epoch:  45 | train loss: 2.5118 | train accuracy: 90.368 | test loss: 1.2841 | test accuracy: 68.770 | epoch runtime:   5.60 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:43:31,241 - INFO - Evaluate Summary Time 1.72s\tLoss 1.2823\t Acc@1 68.4800\t Acc@5 95.3700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:43:31,242 - INFO - Head 81.867\tMid 61.400\tTail 64.533\u001b[0m\n",
      "\u001b[32m2024-10-06 14:43:31,242 - INFO - epoch:  46 | train loss: 2.5097 | train accuracy: 90.891 | test loss: 1.2823 | test accuracy: 68.480 | epoch runtime:   5.53 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:43:36,877 - INFO - Evaluate Summary Time 1.74s\tLoss 1.3070\t Acc@1 66.2800\t Acc@5 95.1700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:43:36,878 - INFO - Head 83.367\tMid 58.425\tTail 59.667\u001b[0m\n",
      "\u001b[32m2024-10-06 14:43:36,878 - INFO - epoch:  47 | train loss: 2.5068 | train accuracy: 91.890 | test loss: 1.3070 | test accuracy: 66.280 | epoch runtime:   5.64 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:43:42,451 - INFO - Evaluate Summary Time 1.73s\tLoss 1.2847\t Acc@1 67.3000\t Acc@5 95.3100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:43:42,451 - INFO - Head 83.000\tMid 60.900\tTail 60.133\u001b[0m\n",
      "\u001b[32m2024-10-06 14:43:42,452 - INFO - epoch:  48 | train loss: 2.5042 | train accuracy: 92.081 | test loss: 1.2847 | test accuracy: 67.300 | epoch runtime:   5.57 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:43:48,055 - INFO - Evaluate Summary Time 1.75s\tLoss 1.3354\t Acc@1 66.0500\t Acc@5 94.8800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:43:48,055 - INFO - Head 81.967\tMid 58.825\tTail 59.767\u001b[0m\n",
      "\u001b[32m2024-10-06 14:43:48,056 - INFO - epoch:  49 | train loss: 2.5000 | train accuracy: 92.722 | test loss: 1.3354 | test accuracy: 66.050 | epoch runtime:   5.60 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:43:53,617 - INFO - Evaluate Summary Time 1.70s\tLoss 1.3309\t Acc@1 65.3800\t Acc@5 94.9300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:43:53,618 - INFO - Head 83.367\tMid 57.175\tTail 58.333\u001b[0m\n",
      "\u001b[32m2024-10-06 14:43:53,618 - INFO - epoch:  50 | train loss: 2.4989 | train accuracy: 93.387 | test loss: 1.3309 | test accuracy: 65.380 | epoch runtime:   5.56 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:43:59,108 - INFO - Evaluate Summary Time 1.64s\tLoss 1.3199\t Acc@1 66.5100\t Acc@5 94.9100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:43:59,108 - INFO - Head 84.067\tMid 59.250\tTail 58.633\u001b[0m\n",
      "\u001b[32m2024-10-06 14:43:59,109 - INFO - epoch:  51 | train loss: 2.4959 | train accuracy: 93.857 | test loss: 1.3199 | test accuracy: 66.510 | epoch runtime:   5.49 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:44:04,675 - INFO - Evaluate Summary Time 1.73s\tLoss 1.3068\t Acc@1 66.5400\t Acc@5 94.7600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:44:04,675 - INFO - Head 84.033\tMid 56.000\tTail 63.100\u001b[0m\n",
      "\u001b[32m2024-10-06 14:44:04,676 - INFO - epoch:  52 | train loss: 2.4942 | train accuracy: 93.999 | test loss: 1.3068 | test accuracy: 66.540 | epoch runtime:   5.57 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:44:10,204 - INFO - Evaluate Summary Time 1.66s\tLoss 1.3060\t Acc@1 67.1200\t Acc@5 94.7600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:44:10,204 - INFO - Head 83.933\tMid 60.525\tTail 59.100\u001b[0m\n",
      "\u001b[32m2024-10-06 14:44:10,204 - INFO - epoch:  53 | train loss: 2.4892 | train accuracy: 94.631 | test loss: 1.3060 | test accuracy: 67.120 | epoch runtime:   5.53 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:44:15,663 - INFO - Evaluate Summary Time 1.66s\tLoss 1.3166\t Acc@1 66.2500\t Acc@5 94.5500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:44:15,663 - INFO - Head 84.333\tMid 58.475\tTail 58.533\u001b[0m\n",
      "\u001b[32m2024-10-06 14:44:15,663 - INFO - epoch:  54 | train loss: 2.4887 | train accuracy: 95.017 | test loss: 1.3166 | test accuracy: 66.250 | epoch runtime:   5.46 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:44:21,165 - INFO - Evaluate Summary Time 1.68s\tLoss 1.3064\t Acc@1 67.7500\t Acc@5 94.6100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:44:21,165 - INFO - Head 80.967\tMid 62.000\tTail 62.200\u001b[0m\n",
      "\u001b[32m2024-10-06 14:44:21,165 - INFO - epoch:  55 | train loss: 2.4865 | train accuracy: 95.561 | test loss: 1.3064 | test accuracy: 67.750 | epoch runtime:   5.50 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:44:26,680 - INFO - Evaluate Summary Time 1.65s\tLoss 1.2930\t Acc@1 67.2500\t Acc@5 94.8300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:44:26,681 - INFO - Head 81.733\tMid 62.775\tTail 58.733\u001b[0m\n",
      "\u001b[32m2024-10-06 14:44:26,681 - INFO - epoch:  56 | train loss: 2.4850 | train accuracy: 95.923 | test loss: 1.2930 | test accuracy: 67.250 | epoch runtime:   5.52 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:44:32,214 - INFO - Evaluate Summary Time 1.65s\tLoss 1.2960\t Acc@1 67.5000\t Acc@5 94.9800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:44:32,215 - INFO - Head 83.233\tMid 59.575\tTail 62.333\u001b[0m\n",
      "\u001b[32m2024-10-06 14:44:32,215 - INFO - epoch:  57 | train loss: 2.4814 | train accuracy: 96.197 | test loss: 1.2960 | test accuracy: 67.500 | epoch runtime:   5.53 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:44:37,742 - INFO - Evaluate Summary Time 1.66s\tLoss 1.3070\t Acc@1 67.0500\t Acc@5 94.5400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:44:37,742 - INFO - Head 83.867\tMid 58.775\tTail 61.267\u001b[0m\n",
      "\u001b[32m2024-10-06 14:44:37,742 - INFO - epoch:  58 | train loss: 2.4789 | train accuracy: 96.466 | test loss: 1.3070 | test accuracy: 67.050 | epoch runtime:   5.53 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:44:43,327 - INFO - Evaluate Summary Time 1.72s\tLoss 1.2981\t Acc@1 66.7100\t Acc@5 94.8000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:44:43,328 - INFO - Head 83.167\tMid 59.300\tTail 60.133\u001b[0m\n",
      "\u001b[32m2024-10-06 14:44:43,328 - INFO - epoch:  59 | train loss: 2.4800 | train accuracy: 96.549 | test loss: 1.2981 | test accuracy: 66.710 | epoch runtime:   5.59 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:44:48,896 - INFO - Evaluate Summary Time 1.73s\tLoss 1.3347\t Acc@1 65.9900\t Acc@5 94.0200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:44:48,896 - INFO - Head 85.633\tMid 56.900\tTail 58.467\u001b[0m\n",
      "\u001b[32m2024-10-06 14:44:48,896 - INFO - epoch:  60 | train loss: 2.4757 | train accuracy: 96.936 | test loss: 1.3347 | test accuracy: 65.990 | epoch runtime:   5.57 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:44:54,472 - INFO - Evaluate Summary Time 1.77s\tLoss 1.3364\t Acc@1 65.0100\t Acc@5 94.6200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:44:54,473 - INFO - Head 83.467\tMid 56.550\tTail 57.833\u001b[0m\n",
      "\u001b[32m2024-10-06 14:44:54,473 - INFO - epoch:  61 | train loss: 2.4747 | train accuracy: 97.142 | test loss: 1.3364 | test accuracy: 65.010 | epoch runtime:   5.58 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:44:59,956 - INFO - Evaluate Summary Time 1.68s\tLoss 1.3297\t Acc@1 65.7200\t Acc@5 94.7200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:44:59,956 - INFO - Head 82.967\tMid 59.475\tTail 56.800\u001b[0m\n",
      "\u001b[32m2024-10-06 14:44:59,956 - INFO - epoch:  62 | train loss: 2.4735 | train accuracy: 97.337 | test loss: 1.3297 | test accuracy: 65.720 | epoch runtime:   5.48 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:45:05,349 - INFO - Evaluate Summary Time 1.63s\tLoss 1.3240\t Acc@1 65.2700\t Acc@5 94.3500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:45:05,349 - INFO - Head 81.667\tMid 54.450\tTail 63.300\u001b[0m\n",
      "\u001b[32m2024-10-06 14:45:05,349 - INFO - epoch:  63 | train loss: 2.4708 | train accuracy: 97.484 | test loss: 1.3240 | test accuracy: 65.270 | epoch runtime:   5.39 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:45:10,826 - INFO - Evaluate Summary Time 1.68s\tLoss 1.2950\t Acc@1 67.5700\t Acc@5 94.9400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:45:10,826 - INFO - Head 85.467\tMid 59.250\tTail 60.767\u001b[0m\n",
      "\u001b[32m2024-10-06 14:45:10,827 - INFO - epoch:  64 | train loss: 2.4717 | train accuracy: 97.533 | test loss: 1.2950 | test accuracy: 67.570 | epoch runtime:   5.48 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:45:16,274 - INFO - Evaluate Summary Time 1.65s\tLoss 1.3235\t Acc@1 66.0300\t Acc@5 94.3700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:45:16,274 - INFO - Head 83.500\tMid 59.150\tTail 57.733\u001b[0m\n",
      "\u001b[32m2024-10-06 14:45:16,274 - INFO - epoch:  65 | train loss: 2.4682 | train accuracy: 97.802 | test loss: 1.3235 | test accuracy: 66.030 | epoch runtime:   5.45 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:45:21,812 - INFO - Evaluate Summary Time 1.68s\tLoss 1.3143\t Acc@1 66.0500\t Acc@5 94.4700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:45:21,812 - INFO - Head 83.267\tMid 56.225\tTail 61.933\u001b[0m\n",
      "\u001b[32m2024-10-06 14:45:21,812 - INFO - epoch:  66 | train loss: 2.4679 | train accuracy: 97.778 | test loss: 1.3143 | test accuracy: 66.050 | epoch runtime:   5.54 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:45:27,432 - INFO - Evaluate Summary Time 1.75s\tLoss 1.2820\t Acc@1 68.0100\t Acc@5 95.1200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:45:27,433 - INFO - Head 83.933\tMid 58.750\tTail 64.433\u001b[0m\n",
      "\u001b[32m2024-10-06 14:45:27,433 - INFO - epoch:  67 | train loss: 2.4664 | train accuracy: 98.091 | test loss: 1.2820 | test accuracy: 68.010 | epoch runtime:   5.62 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:45:33,051 - INFO - Evaluate Summary Time 1.77s\tLoss 1.3108\t Acc@1 66.5500\t Acc@5 94.7700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:45:33,052 - INFO - Head 83.300\tMid 59.625\tTail 59.033\u001b[0m\n",
      "\u001b[32m2024-10-06 14:45:33,052 - INFO - epoch:  68 | train loss: 2.4639 | train accuracy: 98.091 | test loss: 1.3108 | test accuracy: 66.550 | epoch runtime:   5.62 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:45:38,674 - INFO - Evaluate Summary Time 1.70s\tLoss 1.3364\t Acc@1 64.7400\t Acc@5 94.2300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:45:38,674 - INFO - Head 82.600\tMid 57.950\tTail 55.933\u001b[0m\n",
      "\u001b[32m2024-10-06 14:45:38,674 - INFO - epoch:  69 | train loss: 2.4643 | train accuracy: 98.165 | test loss: 1.3364 | test accuracy: 64.740 | epoch runtime:   5.62 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:45:44,140 - INFO - Evaluate Summary Time 1.72s\tLoss 1.3192\t Acc@1 65.4500\t Acc@5 94.2300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:45:44,141 - INFO - Head 82.700\tMid 53.875\tTail 63.633\u001b[0m\n",
      "\u001b[32m2024-10-06 14:45:44,141 - INFO - epoch:  70 | train loss: 2.4622 | train accuracy: 98.302 | test loss: 1.3192 | test accuracy: 65.450 | epoch runtime:   5.47 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:45:49,747 - INFO - Evaluate Summary Time 1.72s\tLoss 1.3410\t Acc@1 65.5100\t Acc@5 94.2400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:45:49,747 - INFO - Head 84.800\tMid 56.625\tTail 58.067\u001b[0m\n",
      "\u001b[32m2024-10-06 14:45:49,747 - INFO - epoch:  71 | train loss: 2.4609 | train accuracy: 98.488 | test loss: 1.3410 | test accuracy: 65.510 | epoch runtime:   5.61 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:45:55,417 - INFO - Evaluate Summary Time 1.77s\tLoss 1.2860\t Acc@1 67.9600\t Acc@5 94.6100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:45:55,417 - INFO - Head 81.767\tMid 59.600\tTail 65.300\u001b[0m\n",
      "\u001b[32m2024-10-06 14:45:55,418 - INFO - epoch:  72 | train loss: 2.4610 | train accuracy: 98.517 | test loss: 1.2860 | test accuracy: 67.960 | epoch runtime:   5.67 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:46:01,034 - INFO - Evaluate Summary Time 1.76s\tLoss 1.3308\t Acc@1 65.6100\t Acc@5 94.2700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:46:01,035 - INFO - Head 83.000\tMid 57.675\tTail 58.800\u001b[0m\n",
      "\u001b[32m2024-10-06 14:46:01,035 - INFO - epoch:  73 | train loss: 2.4599 | train accuracy: 98.576 | test loss: 1.3308 | test accuracy: 65.610 | epoch runtime:   5.62 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:46:06,645 - INFO - Evaluate Summary Time 1.67s\tLoss 1.3232\t Acc@1 65.9900\t Acc@5 94.2400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:46:06,645 - INFO - Head 84.967\tMid 57.825\tTail 57.900\u001b[0m\n",
      "\u001b[32m2024-10-06 14:46:06,645 - INFO - epoch:  74 | train loss: 2.4588 | train accuracy: 98.541 | test loss: 1.3232 | test accuracy: 65.990 | epoch runtime:   5.61 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:46:12,254 - INFO - Evaluate Summary Time 1.67s\tLoss 1.3367\t Acc@1 65.6400\t Acc@5 94.2300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:46:12,255 - INFO - Head 84.100\tMid 59.675\tTail 55.133\u001b[0m\n",
      "\u001b[32m2024-10-06 14:46:12,255 - INFO - epoch:  75 | train loss: 2.4586 | train accuracy: 98.615 | test loss: 1.3367 | test accuracy: 65.640 | epoch runtime:   5.61 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:46:17,874 - INFO - Evaluate Summary Time 1.62s\tLoss 1.3178\t Acc@1 66.8900\t Acc@5 94.3200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:46:17,874 - INFO - Head 83.367\tMid 60.400\tTail 59.067\u001b[0m\n",
      "\u001b[32m2024-10-06 14:46:17,875 - INFO - epoch:  76 | train loss: 2.4582 | train accuracy: 98.732 | test loss: 1.3178 | test accuracy: 66.890 | epoch runtime:   5.62 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:46:23,585 - INFO - Evaluate Summary Time 1.70s\tLoss 1.2952\t Acc@1 66.6400\t Acc@5 94.6300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:46:23,585 - INFO - Head 83.800\tMid 59.475\tTail 59.033\u001b[0m\n",
      "\u001b[32m2024-10-06 14:46:23,586 - INFO - epoch:  77 | train loss: 2.4569 | train accuracy: 98.801 | test loss: 1.2952 | test accuracy: 66.640 | epoch runtime:   5.71 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:46:29,122 - INFO - Evaluate Summary Time 1.70s\tLoss 1.2986\t Acc@1 67.0500\t Acc@5 94.7500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:46:29,122 - INFO - Head 84.233\tMid 58.375\tTail 61.433\u001b[0m\n",
      "\u001b[32m2024-10-06 14:46:29,123 - INFO - epoch:  78 | train loss: 2.4546 | train accuracy: 98.835 | test loss: 1.2986 | test accuracy: 67.050 | epoch runtime:   5.54 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:46:34,602 - INFO - Evaluate Summary Time 1.65s\tLoss 1.3140\t Acc@1 66.2700\t Acc@5 94.1100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:46:34,603 - INFO - Head 83.700\tMid 58.250\tTail 59.533\u001b[0m\n",
      "\u001b[32m2024-10-06 14:46:34,603 - INFO - epoch:  79 | train loss: 2.4526 | train accuracy: 98.752 | test loss: 1.3140 | test accuracy: 66.270 | epoch runtime:   5.48 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:46:40,129 - INFO - Evaluate Summary Time 1.71s\tLoss 1.3478\t Acc@1 63.7600\t Acc@5 94.1900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:46:40,129 - INFO - Head 80.567\tMid 57.850\tTail 54.833\u001b[0m\n",
      "\u001b[32m2024-10-06 14:46:40,129 - INFO - epoch:  80 | train loss: 2.4537 | train accuracy: 98.850 | test loss: 1.3478 | test accuracy: 63.760 | epoch runtime:   5.53 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:46:45,616 - INFO - Evaluate Summary Time 1.61s\tLoss 1.3156\t Acc@1 66.2300\t Acc@5 94.8300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:46:45,617 - INFO - Head 82.533\tMid 59.400\tTail 59.033\u001b[0m\n",
      "\u001b[32m2024-10-06 14:46:45,617 - INFO - epoch:  81 | train loss: 2.4432 | train accuracy: 99.271 | test loss: 1.3156 | test accuracy: 66.230 | epoch runtime:   5.49 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:46:51,170 - INFO - Evaluate Summary Time 1.76s\tLoss 1.3096\t Acc@1 66.5100\t Acc@5 94.7600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:46:51,171 - INFO - Head 83.900\tMid 59.675\tTail 58.233\u001b[0m\n",
      "\u001b[32m2024-10-06 14:46:51,171 - INFO - epoch:  82 | train loss: 2.4395 | train accuracy: 99.295 | test loss: 1.3096 | test accuracy: 66.510 | epoch runtime:   5.55 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:46:56,732 - INFO - Evaluate Summary Time 1.70s\tLoss 1.2991\t Acc@1 67.6300\t Acc@5 94.8000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:46:56,732 - INFO - Head 85.500\tMid 59.525\tTail 60.567\u001b[0m\n",
      "\u001b[32m2024-10-06 14:46:56,732 - INFO - epoch:  83 | train loss: 2.4370 | train accuracy: 99.393 | test loss: 1.2991 | test accuracy: 67.630 | epoch runtime:   5.56 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:47:02,242 - INFO - Evaluate Summary Time 1.63s\tLoss 1.2953\t Acc@1 67.3700\t Acc@5 94.9000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:47:02,242 - INFO - Head 83.233\tMid 59.925\tTail 61.433\u001b[0m\n",
      "\u001b[32m2024-10-06 14:47:02,242 - INFO - epoch:  84 | train loss: 2.4361 | train accuracy: 99.481 | test loss: 1.2953 | test accuracy: 67.370 | epoch runtime:   5.51 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:47:07,749 - INFO - Evaluate Summary Time 1.65s\tLoss 1.2979\t Acc@1 66.8000\t Acc@5 94.8200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:47:07,750 - INFO - Head 84.600\tMid 58.925\tTail 59.500\u001b[0m\n",
      "\u001b[32m2024-10-06 14:47:07,750 - INFO - epoch:  85 | train loss: 2.4359 | train accuracy: 99.481 | test loss: 1.2979 | test accuracy: 66.800 | epoch runtime:   5.51 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:47:13,252 - INFO - Evaluate Summary Time 1.75s\tLoss 1.3166\t Acc@1 66.4400\t Acc@5 94.4100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:47:13,253 - INFO - Head 83.600\tMid 59.425\tTail 58.633\u001b[0m\n",
      "\u001b[32m2024-10-06 14:47:13,253 - INFO - epoch:  86 | train loss: 2.4347 | train accuracy: 99.506 | test loss: 1.3166 | test accuracy: 66.440 | epoch runtime:   5.50 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:47:18,804 - INFO - Evaluate Summary Time 1.65s\tLoss 1.3088\t Acc@1 66.1400\t Acc@5 94.6600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:47:18,804 - INFO - Head 81.700\tMid 60.375\tTail 58.267\u001b[0m\n",
      "\u001b[32m2024-10-06 14:47:18,804 - INFO - epoch:  87 | train loss: 2.4332 | train accuracy: 99.530 | test loss: 1.3088 | test accuracy: 66.140 | epoch runtime:   5.55 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:47:24,279 - INFO - Evaluate Summary Time 1.68s\tLoss 1.3142\t Acc@1 66.7100\t Acc@5 94.4100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:47:24,280 - INFO - Head 83.067\tMid 59.575\tTail 59.867\u001b[0m\n",
      "\u001b[32m2024-10-06 14:47:24,280 - INFO - epoch:  88 | train loss: 2.4350 | train accuracy: 99.555 | test loss: 1.3142 | test accuracy: 66.710 | epoch runtime:   5.48 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:47:29,858 - INFO - Evaluate Summary Time 1.76s\tLoss 1.3158\t Acc@1 66.4900\t Acc@5 94.4300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:47:29,858 - INFO - Head 82.900\tMid 60.400\tTail 58.200\u001b[0m\n",
      "\u001b[32m2024-10-06 14:47:29,859 - INFO - epoch:  89 | train loss: 2.4333 | train accuracy: 99.584 | test loss: 1.3158 | test accuracy: 66.490 | epoch runtime:   5.58 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:47:35,349 - INFO - Evaluate Summary Time 1.66s\tLoss 1.3145\t Acc@1 65.6100\t Acc@5 94.6400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:47:35,350 - INFO - Head 83.533\tMid 59.800\tTail 55.433\u001b[0m\n",
      "\u001b[32m2024-10-06 14:47:35,350 - INFO - epoch:  90 | train loss: 2.4335 | train accuracy: 99.525 | test loss: 1.3145 | test accuracy: 65.610 | epoch runtime:   5.49 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:47:40,825 - INFO - Evaluate Summary Time 1.72s\tLoss 1.3129\t Acc@1 66.1300\t Acc@5 94.6000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:47:40,826 - INFO - Head 84.800\tMid 58.175\tTail 58.067\u001b[0m\n",
      "\u001b[32m2024-10-06 14:47:40,826 - INFO - epoch:  91 | train loss: 2.4326 | train accuracy: 99.584 | test loss: 1.3129 | test accuracy: 66.130 | epoch runtime:   5.48 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:47:46,472 - INFO - Evaluate Summary Time 1.79s\tLoss 1.3156\t Acc@1 66.8800\t Acc@5 94.5700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:47:46,473 - INFO - Head 84.067\tMid 59.975\tTail 58.900\u001b[0m\n",
      "\u001b[32m2024-10-06 14:47:46,473 - INFO - epoch:  92 | train loss: 2.4320 | train accuracy: 99.599 | test loss: 1.3156 | test accuracy: 66.880 | epoch runtime:   5.65 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:47:52,079 - INFO - Evaluate Summary Time 1.79s\tLoss 1.3089\t Acc@1 66.6900\t Acc@5 94.3300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:47:52,079 - INFO - Head 84.033\tMid 59.400\tTail 59.067\u001b[0m\n",
      "\u001b[32m2024-10-06 14:47:52,080 - INFO - epoch:  93 | train loss: 2.4322 | train accuracy: 99.628 | test loss: 1.3089 | test accuracy: 66.690 | epoch runtime:   5.61 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:47:57,763 - INFO - Evaluate Summary Time 1.72s\tLoss 1.3003\t Acc@1 66.9000\t Acc@5 94.3300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:47:57,763 - INFO - Head 83.300\tMid 59.800\tTail 59.967\u001b[0m\n",
      "\u001b[32m2024-10-06 14:47:57,764 - INFO - epoch:  94 | train loss: 2.4308 | train accuracy: 99.652 | test loss: 1.3003 | test accuracy: 66.900 | epoch runtime:   5.68 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:48:03,253 - INFO - Evaluate Summary Time 1.69s\tLoss 1.3025\t Acc@1 66.2500\t Acc@5 94.9300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:48:03,253 - INFO - Head 84.067\tMid 59.800\tTail 57.033\u001b[0m\n",
      "\u001b[32m2024-10-06 14:48:03,253 - INFO - epoch:  95 | train loss: 2.4306 | train accuracy: 99.628 | test loss: 1.3025 | test accuracy: 66.250 | epoch runtime:   5.49 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:48:08,785 - INFO - Evaluate Summary Time 1.69s\tLoss 1.3001\t Acc@1 66.8200\t Acc@5 94.7200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:48:08,785 - INFO - Head 82.533\tMid 60.750\tTail 59.200\u001b[0m\n",
      "\u001b[32m2024-10-06 14:48:08,785 - INFO - epoch:  96 | train loss: 2.4290 | train accuracy: 99.692 | test loss: 1.3001 | test accuracy: 66.820 | epoch runtime:   5.53 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:48:14,271 - INFO - Evaluate Summary Time 1.71s\tLoss 1.3073\t Acc@1 66.4100\t Acc@5 94.5900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:48:14,272 - INFO - Head 83.900\tMid 60.050\tTail 57.400\u001b[0m\n",
      "\u001b[32m2024-10-06 14:48:14,272 - INFO - epoch:  97 | train loss: 2.4289 | train accuracy: 99.648 | test loss: 1.3073 | test accuracy: 66.410 | epoch runtime:   5.49 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:48:19,788 - INFO - Evaluate Summary Time 1.75s\tLoss 1.3019\t Acc@1 66.7500\t Acc@5 94.6400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:48:19,788 - INFO - Head 85.100\tMid 59.575\tTail 57.967\u001b[0m\n",
      "\u001b[32m2024-10-06 14:48:19,788 - INFO - epoch:  98 | train loss: 2.4301 | train accuracy: 99.677 | test loss: 1.3019 | test accuracy: 66.750 | epoch runtime:   5.52 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:48:25,439 - INFO - Evaluate Summary Time 1.77s\tLoss 1.3190\t Acc@1 65.8100\t Acc@5 94.6800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:48:25,440 - INFO - Head 83.267\tMid 57.475\tTail 59.467\u001b[0m\n",
      "\u001b[32m2024-10-06 14:48:25,440 - INFO - epoch:  99 | train loss: 2.4286 | train accuracy: 99.706 | test loss: 1.3190 | test accuracy: 65.810 | epoch runtime:   5.65 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:48:30,981 - INFO - Evaluate Summary Time 1.66s\tLoss 1.2952\t Acc@1 67.3000\t Acc@5 94.8800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:48:30,981 - INFO - Head 84.400\tMid 60.675\tTail 59.033\u001b[0m\n",
      "\u001b[32m2024-10-06 14:48:30,982 - INFO - epoch: 100 | train loss: 2.4277 | train accuracy: 99.682 | test loss: 1.2952 | test accuracy: 67.300 | epoch runtime:   5.54 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:48:36,614 - INFO - Evaluate Summary Time 1.77s\tLoss 1.3055\t Acc@1 66.2900\t Acc@5 94.8500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:48:36,614 - INFO - Head 83.000\tMid 59.675\tTail 58.400\u001b[0m\n",
      "\u001b[32m2024-10-06 14:48:36,614 - INFO - epoch: 101 | train loss: 2.4272 | train accuracy: 99.736 | test loss: 1.3055 | test accuracy: 66.290 | epoch runtime:   5.63 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:48:42,227 - INFO - Evaluate Summary Time 1.82s\tLoss 1.3067\t Acc@1 66.7600\t Acc@5 94.6700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:48:42,227 - INFO - Head 84.467\tMid 58.350\tTail 60.267\u001b[0m\n",
      "\u001b[32m2024-10-06 14:48:42,228 - INFO - epoch: 102 | train loss: 2.4272 | train accuracy: 99.706 | test loss: 1.3067 | test accuracy: 66.760 | epoch runtime:   5.61 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:48:47,856 - INFO - Evaluate Summary Time 1.74s\tLoss 1.3156\t Acc@1 66.2400\t Acc@5 93.9000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:48:47,857 - INFO - Head 82.500\tMid 60.325\tTail 57.867\u001b[0m\n",
      "\u001b[32m2024-10-06 14:48:47,857 - INFO - epoch: 103 | train loss: 2.4265 | train accuracy: 99.731 | test loss: 1.3156 | test accuracy: 66.240 | epoch runtime:   5.63 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:48:53,483 - INFO - Evaluate Summary Time 1.78s\tLoss 1.2933\t Acc@1 67.0300\t Acc@5 94.8300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:48:53,483 - INFO - Head 84.700\tMid 58.450\tTail 60.800\u001b[0m\n",
      "\u001b[32m2024-10-06 14:48:53,484 - INFO - epoch: 104 | train loss: 2.4261 | train accuracy: 99.721 | test loss: 1.2933 | test accuracy: 67.030 | epoch runtime:   5.63 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:48:59,083 - INFO - Evaluate Summary Time 1.77s\tLoss 1.3052\t Acc@1 66.7700\t Acc@5 94.5400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:48:59,084 - INFO - Head 82.567\tMid 61.900\tTail 57.467\u001b[0m\n",
      "\u001b[32m2024-10-06 14:48:59,084 - INFO - epoch: 105 | train loss: 2.4258 | train accuracy: 99.765 | test loss: 1.3052 | test accuracy: 66.770 | epoch runtime:   5.60 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:49:04,668 - INFO - Evaluate Summary Time 1.73s\tLoss 1.2947\t Acc@1 67.1900\t Acc@5 94.6100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:49:04,668 - INFO - Head 82.967\tMid 59.225\tTail 62.033\u001b[0m\n",
      "\u001b[32m2024-10-06 14:49:04,669 - INFO - epoch: 106 | train loss: 2.4252 | train accuracy: 99.755 | test loss: 1.2947 | test accuracy: 67.190 | epoch runtime:   5.58 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:49:10,212 - INFO - Evaluate Summary Time 1.68s\tLoss 1.2924\t Acc@1 67.2100\t Acc@5 94.7600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:49:10,212 - INFO - Head 84.367\tMid 58.475\tTail 61.700\u001b[0m\n",
      "\u001b[32m2024-10-06 14:49:10,213 - INFO - epoch: 107 | train loss: 2.4243 | train accuracy: 99.726 | test loss: 1.2924 | test accuracy: 67.210 | epoch runtime:   5.54 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:49:15,863 - INFO - Evaluate Summary Time 1.76s\tLoss 1.3103\t Acc@1 66.1300\t Acc@5 94.6200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:49:15,863 - INFO - Head 83.400\tMid 59.275\tTail 58.000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:49:15,863 - INFO - epoch: 108 | train loss: 2.4236 | train accuracy: 99.770 | test loss: 1.3103 | test accuracy: 66.130 | epoch runtime:   5.65 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:49:21,416 - INFO - Evaluate Summary Time 1.77s\tLoss 1.3192\t Acc@1 66.3600\t Acc@5 94.6800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:49:21,417 - INFO - Head 84.433\tMid 59.500\tTail 57.433\u001b[0m\n",
      "\u001b[32m2024-10-06 14:49:21,417 - INFO - epoch: 109 | train loss: 2.4236 | train accuracy: 99.736 | test loss: 1.3192 | test accuracy: 66.360 | epoch runtime:   5.55 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:49:26,915 - INFO - Evaluate Summary Time 1.70s\tLoss 1.3080\t Acc@1 66.7900\t Acc@5 94.4100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:49:26,915 - INFO - Head 84.500\tMid 58.025\tTail 60.767\u001b[0m\n",
      "\u001b[32m2024-10-06 14:49:26,916 - INFO - epoch: 110 | train loss: 2.4229 | train accuracy: 99.760 | test loss: 1.3080 | test accuracy: 66.790 | epoch runtime:   5.50 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:49:32,489 - INFO - Evaluate Summary Time 1.72s\tLoss 1.3086\t Acc@1 66.2300\t Acc@5 94.7400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:49:32,489 - INFO - Head 84.067\tMid 58.500\tTail 58.700\u001b[0m\n",
      "\u001b[32m2024-10-06 14:49:32,489 - INFO - epoch: 111 | train loss: 2.4228 | train accuracy: 99.760 | test loss: 1.3086 | test accuracy: 66.230 | epoch runtime:   5.57 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:49:38,145 - INFO - Evaluate Summary Time 1.74s\tLoss 1.2981\t Acc@1 66.6900\t Acc@5 94.5300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:49:38,145 - INFO - Head 84.133\tMid 61.150\tTail 56.633\u001b[0m\n",
      "\u001b[32m2024-10-06 14:49:38,146 - INFO - epoch: 112 | train loss: 2.4236 | train accuracy: 99.760 | test loss: 1.2981 | test accuracy: 66.690 | epoch runtime:   5.66 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:49:43,692 - INFO - Evaluate Summary Time 1.77s\tLoss 1.2843\t Acc@1 67.6400\t Acc@5 94.8700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:49:43,693 - INFO - Head 84.567\tMid 59.325\tTail 61.800\u001b[0m\n",
      "\u001b[32m2024-10-06 14:49:43,693 - INFO - epoch: 113 | train loss: 2.4236 | train accuracy: 99.785 | test loss: 1.2843 | test accuracy: 67.640 | epoch runtime:   5.55 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:49:49,268 - INFO - Evaluate Summary Time 1.70s\tLoss 1.3126\t Acc@1 66.1900\t Acc@5 94.4500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:49:49,269 - INFO - Head 83.900\tMid 57.325\tTail 60.300\u001b[0m\n",
      "\u001b[32m2024-10-06 14:49:49,269 - INFO - epoch: 114 | train loss: 2.4216 | train accuracy: 99.780 | test loss: 1.3126 | test accuracy: 66.190 | epoch runtime:   5.58 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:49:54,937 - INFO - Evaluate Summary Time 1.75s\tLoss 1.3220\t Acc@1 66.0100\t Acc@5 94.3000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:49:54,938 - INFO - Head 84.633\tMid 58.450\tTail 57.467\u001b[0m\n",
      "\u001b[32m2024-10-06 14:49:54,938 - INFO - epoch: 115 | train loss: 2.4215 | train accuracy: 99.741 | test loss: 1.3220 | test accuracy: 66.010 | epoch runtime:   5.67 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:50:00,558 - INFO - Evaluate Summary Time 1.70s\tLoss 1.3111\t Acc@1 66.4000\t Acc@5 94.5400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:50:00,558 - INFO - Head 83.467\tMid 58.400\tTail 60.000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:50:00,558 - INFO - epoch: 116 | train loss: 2.4229 | train accuracy: 99.780 | test loss: 1.3111 | test accuracy: 66.400 | epoch runtime:   5.62 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:50:06,081 - INFO - Evaluate Summary Time 1.68s\tLoss 1.3113\t Acc@1 66.4200\t Acc@5 94.6400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:50:06,082 - INFO - Head 83.400\tMid 59.600\tTail 58.533\u001b[0m\n",
      "\u001b[32m2024-10-06 14:50:06,082 - INFO - epoch: 117 | train loss: 2.4224 | train accuracy: 99.780 | test loss: 1.3113 | test accuracy: 66.420 | epoch runtime:   5.52 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:50:11,540 - INFO - Evaluate Summary Time 1.62s\tLoss 1.2997\t Acc@1 66.7500\t Acc@5 94.8800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:50:11,540 - INFO - Head 84.367\tMid 57.925\tTail 60.900\u001b[0m\n",
      "\u001b[32m2024-10-06 14:50:11,540 - INFO - epoch: 118 | train loss: 2.4216 | train accuracy: 99.794 | test loss: 1.2997 | test accuracy: 66.750 | epoch runtime:   5.46 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:50:17,126 - INFO - Evaluate Summary Time 1.70s\tLoss 1.3027\t Acc@1 66.8800\t Acc@5 94.7500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:50:17,126 - INFO - Head 83.800\tMid 60.000\tTail 59.133\u001b[0m\n",
      "\u001b[32m2024-10-06 14:50:17,126 - INFO - epoch: 119 | train loss: 2.4211 | train accuracy: 99.824 | test loss: 1.3027 | test accuracy: 66.880 | epoch runtime:   5.59 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:50:22,736 - INFO - Evaluate Summary Time 1.74s\tLoss 1.3096\t Acc@1 66.3400\t Acc@5 94.7300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:50:22,736 - INFO - Head 82.233\tMid 60.850\tTail 57.767\u001b[0m\n",
      "\u001b[32m2024-10-06 14:50:22,736 - INFO - epoch: 120 | train loss: 2.4219 | train accuracy: 99.809 | test loss: 1.3096 | test accuracy: 66.340 | epoch runtime:   5.61 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:50:28,220 - INFO - Evaluate Summary Time 1.62s\tLoss 1.3163\t Acc@1 66.4800\t Acc@5 94.6700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:50:28,220 - INFO - Head 84.567\tMid 60.650\tTail 56.167\u001b[0m\n",
      "\u001b[32m2024-10-06 14:50:28,221 - INFO - epoch: 121 | train loss: 2.4209 | train accuracy: 99.794 | test loss: 1.3163 | test accuracy: 66.480 | epoch runtime:   5.48 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:50:33,787 - INFO - Evaluate Summary Time 1.73s\tLoss 1.3053\t Acc@1 66.6300\t Acc@5 94.8800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:50:33,787 - INFO - Head 84.100\tMid 59.150\tTail 59.133\u001b[0m\n",
      "\u001b[32m2024-10-06 14:50:33,787 - INFO - epoch: 122 | train loss: 2.4203 | train accuracy: 99.790 | test loss: 1.3053 | test accuracy: 66.630 | epoch runtime:   5.57 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:50:39,373 - INFO - Evaluate Summary Time 1.68s\tLoss 1.3067\t Acc@1 67.0300\t Acc@5 94.8100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:50:39,373 - INFO - Head 84.333\tMid 59.000\tTail 60.433\u001b[0m\n",
      "\u001b[32m2024-10-06 14:50:39,373 - INFO - epoch: 123 | train loss: 2.4209 | train accuracy: 99.814 | test loss: 1.3067 | test accuracy: 67.030 | epoch runtime:   5.59 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:50:44,724 - INFO - Evaluate Summary Time 1.60s\tLoss 1.3009\t Acc@1 67.4800\t Acc@5 94.7600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:50:44,725 - INFO - Head 84.033\tMid 60.600\tTail 60.100\u001b[0m\n",
      "\u001b[32m2024-10-06 14:50:44,725 - INFO - epoch: 124 | train loss: 2.4195 | train accuracy: 99.799 | test loss: 1.3009 | test accuracy: 67.480 | epoch runtime:   5.35 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:50:50,301 - INFO - Evaluate Summary Time 1.72s\tLoss 1.3074\t Acc@1 66.2600\t Acc@5 94.5600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:50:50,301 - INFO - Head 83.200\tMid 60.400\tTail 57.133\u001b[0m\n",
      "\u001b[32m2024-10-06 14:50:50,301 - INFO - epoch: 125 | train loss: 2.4191 | train accuracy: 99.819 | test loss: 1.3074 | test accuracy: 66.260 | epoch runtime:   5.58 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:50:55,745 - INFO - Evaluate Summary Time 1.67s\tLoss 1.2980\t Acc@1 66.8200\t Acc@5 94.6000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:50:55,745 - INFO - Head 84.400\tMid 59.700\tTail 58.733\u001b[0m\n",
      "\u001b[32m2024-10-06 14:50:55,745 - INFO - epoch: 126 | train loss: 2.4205 | train accuracy: 99.804 | test loss: 1.2980 | test accuracy: 66.820 | epoch runtime:   5.44 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:51:01,268 - INFO - Evaluate Summary Time 1.66s\tLoss 1.3103\t Acc@1 66.8300\t Acc@5 94.5700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:51:01,268 - INFO - Head 84.467\tMid 59.175\tTail 59.400\u001b[0m\n",
      "\u001b[32m2024-10-06 14:51:01,268 - INFO - epoch: 127 | train loss: 2.4190 | train accuracy: 99.819 | test loss: 1.3103 | test accuracy: 66.830 | epoch runtime:   5.52 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:51:06,732 - INFO - Evaluate Summary Time 1.63s\tLoss 1.3085\t Acc@1 66.9200\t Acc@5 94.5300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:51:06,733 - INFO - Head 84.533\tMid 58.925\tTail 59.967\u001b[0m\n",
      "\u001b[32m2024-10-06 14:51:06,733 - INFO - epoch: 128 | train loss: 2.4199 | train accuracy: 99.858 | test loss: 1.3085 | test accuracy: 66.920 | epoch runtime:   5.46 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:51:12,253 - INFO - Evaluate Summary Time 1.66s\tLoss 1.2949\t Acc@1 67.5500\t Acc@5 94.7600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:51:12,254 - INFO - Head 84.767\tMid 61.800\tTail 58.000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:51:12,254 - INFO - epoch: 129 | train loss: 2.4197 | train accuracy: 99.853 | test loss: 1.2949 | test accuracy: 67.550 | epoch runtime:   5.52 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:51:17,886 - INFO - Evaluate Summary Time 1.82s\tLoss 1.3170\t Acc@1 66.2400\t Acc@5 94.1800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:51:17,887 - INFO - Head 82.500\tMid 59.250\tTail 59.300\u001b[0m\n",
      "\u001b[32m2024-10-06 14:51:17,887 - INFO - epoch: 130 | train loss: 2.4198 | train accuracy: 99.824 | test loss: 1.3170 | test accuracy: 66.240 | epoch runtime:   5.63 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:51:23,546 - INFO - Evaluate Summary Time 1.75s\tLoss 1.3231\t Acc@1 65.4900\t Acc@5 94.4700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:51:23,546 - INFO - Head 83.333\tMid 57.650\tTail 58.100\u001b[0m\n",
      "\u001b[32m2024-10-06 14:51:23,546 - INFO - epoch: 131 | train loss: 2.4190 | train accuracy: 99.848 | test loss: 1.3231 | test accuracy: 65.490 | epoch runtime:   5.66 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:51:29,133 - INFO - Evaluate Summary Time 1.79s\tLoss 1.3150\t Acc@1 65.9300\t Acc@5 94.4800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:51:29,134 - INFO - Head 82.767\tMid 59.525\tTail 57.633\u001b[0m\n",
      "\u001b[32m2024-10-06 14:51:29,134 - INFO - epoch: 132 | train loss: 2.4192 | train accuracy: 99.863 | test loss: 1.3150 | test accuracy: 65.930 | epoch runtime:   5.59 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:51:34,617 - INFO - Evaluate Summary Time 1.66s\tLoss 1.2847\t Acc@1 67.3400\t Acc@5 94.8700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:51:34,618 - INFO - Head 84.800\tMid 60.150\tTail 59.467\u001b[0m\n",
      "\u001b[32m2024-10-06 14:51:34,618 - INFO - epoch: 133 | train loss: 2.4183 | train accuracy: 99.838 | test loss: 1.2847 | test accuracy: 67.340 | epoch runtime:   5.48 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:51:40,269 - INFO - Evaluate Summary Time 1.76s\tLoss 1.3036\t Acc@1 66.8200\t Acc@5 94.5400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:51:40,270 - INFO - Head 83.933\tMid 59.750\tTail 59.133\u001b[0m\n",
      "\u001b[32m2024-10-06 14:51:40,270 - INFO - epoch: 134 | train loss: 2.4193 | train accuracy: 99.838 | test loss: 1.3036 | test accuracy: 66.820 | epoch runtime:   5.65 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:51:45,917 - INFO - Evaluate Summary Time 1.78s\tLoss 1.2999\t Acc@1 67.2000\t Acc@5 94.7200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:51:45,917 - INFO - Head 84.933\tMid 59.400\tTail 59.867\u001b[0m\n",
      "\u001b[32m2024-10-06 14:51:45,918 - INFO - epoch: 135 | train loss: 2.4183 | train accuracy: 99.843 | test loss: 1.2999 | test accuracy: 67.200 | epoch runtime:   5.65 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:51:51,408 - INFO - Evaluate Summary Time 1.69s\tLoss 1.3069\t Acc@1 67.0800\t Acc@5 94.7200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:51:51,409 - INFO - Head 84.367\tMid 60.225\tTail 58.933\u001b[0m\n",
      "\u001b[32m2024-10-06 14:51:51,409 - INFO - epoch: 136 | train loss: 2.4185 | train accuracy: 99.868 | test loss: 1.3069 | test accuracy: 67.080 | epoch runtime:   5.49 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:51:56,980 - INFO - Evaluate Summary Time 1.69s\tLoss 1.2967\t Acc@1 67.6300\t Acc@5 94.8300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:51:56,980 - INFO - Head 85.500\tMid 60.750\tTail 58.933\u001b[0m\n",
      "\u001b[32m2024-10-06 14:51:56,980 - INFO - epoch: 137 | train loss: 2.4182 | train accuracy: 99.863 | test loss: 1.2967 | test accuracy: 67.630 | epoch runtime:   5.57 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:52:02,604 - INFO - Evaluate Summary Time 1.69s\tLoss 1.3149\t Acc@1 66.2100\t Acc@5 94.6600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:52:02,604 - INFO - Head 83.900\tMid 59.075\tTail 58.033\u001b[0m\n",
      "\u001b[32m2024-10-06 14:52:02,604 - INFO - epoch: 138 | train loss: 2.4191 | train accuracy: 99.843 | test loss: 1.3149 | test accuracy: 66.210 | epoch runtime:   5.62 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:52:08,153 - INFO - Evaluate Summary Time 1.64s\tLoss 1.3118\t Acc@1 66.3900\t Acc@5 94.4600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:52:08,154 - INFO - Head 82.833\tMid 60.375\tTail 57.967\u001b[0m\n",
      "\u001b[32m2024-10-06 14:52:08,154 - INFO - epoch: 139 | train loss: 2.4180 | train accuracy: 99.824 | test loss: 1.3118 | test accuracy: 66.390 | epoch runtime:   5.55 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:52:13,639 - INFO - Evaluate Summary Time 1.69s\tLoss 1.3092\t Acc@1 66.7700\t Acc@5 94.8900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:52:13,640 - INFO - Head 82.367\tMid 61.075\tTail 58.767\u001b[0m\n",
      "\u001b[32m2024-10-06 14:52:13,640 - INFO - epoch: 140 | train loss: 2.4193 | train accuracy: 99.829 | test loss: 1.3092 | test accuracy: 66.770 | epoch runtime:   5.49 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:52:19,123 - INFO - Evaluate Summary Time 1.69s\tLoss 1.3115\t Acc@1 66.7200\t Acc@5 94.6400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:52:19,124 - INFO - Head 83.767\tMid 59.650\tTail 59.100\u001b[0m\n",
      "\u001b[32m2024-10-06 14:52:19,124 - INFO - epoch: 141 | train loss: 2.4181 | train accuracy: 99.868 | test loss: 1.3115 | test accuracy: 66.720 | epoch runtime:   5.48 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:52:24,709 - INFO - Evaluate Summary Time 1.76s\tLoss 1.3142\t Acc@1 66.1900\t Acc@5 94.5200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:52:24,710 - INFO - Head 84.633\tMid 59.250\tTail 57.000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:52:24,710 - INFO - epoch: 142 | train loss: 2.4182 | train accuracy: 99.838 | test loss: 1.3142 | test accuracy: 66.190 | epoch runtime:   5.59 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:52:30,340 - INFO - Evaluate Summary Time 1.73s\tLoss 1.3148\t Acc@1 66.8300\t Acc@5 94.5900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:52:30,340 - INFO - Head 85.000\tMid 59.575\tTail 58.333\u001b[0m\n",
      "\u001b[32m2024-10-06 14:52:30,341 - INFO - epoch: 143 | train loss: 2.4179 | train accuracy: 99.829 | test loss: 1.3148 | test accuracy: 66.830 | epoch runtime:   5.63 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:52:35,877 - INFO - Evaluate Summary Time 1.70s\tLoss 1.3242\t Acc@1 65.7100\t Acc@5 94.3000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:52:35,877 - INFO - Head 85.033\tMid 57.900\tTail 56.800\u001b[0m\n",
      "\u001b[32m2024-10-06 14:52:35,878 - INFO - epoch: 144 | train loss: 2.4178 | train accuracy: 99.848 | test loss: 1.3242 | test accuracy: 65.710 | epoch runtime:   5.54 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:52:41,474 - INFO - Evaluate Summary Time 1.70s\tLoss 1.3059\t Acc@1 66.9500\t Acc@5 94.7500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:52:41,474 - INFO - Head 84.533\tMid 60.150\tTail 58.433\u001b[0m\n",
      "\u001b[32m2024-10-06 14:52:41,475 - INFO - epoch: 145 | train loss: 2.4179 | train accuracy: 99.848 | test loss: 1.3059 | test accuracy: 66.950 | epoch runtime:   5.60 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:52:47,008 - INFO - Evaluate Summary Time 1.66s\tLoss 1.3137\t Acc@1 66.5400\t Acc@5 94.3500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:52:47,008 - INFO - Head 83.900\tMid 59.300\tTail 58.833\u001b[0m\n",
      "\u001b[32m2024-10-06 14:52:47,008 - INFO - epoch: 146 | train loss: 2.4180 | train accuracy: 99.863 | test loss: 1.3137 | test accuracy: 66.540 | epoch runtime:   5.53 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:52:52,583 - INFO - Evaluate Summary Time 1.68s\tLoss 1.3130\t Acc@1 66.8800\t Acc@5 94.4300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:52:52,583 - INFO - Head 84.000\tMid 59.175\tTail 60.033\u001b[0m\n",
      "\u001b[32m2024-10-06 14:52:52,584 - INFO - epoch: 147 | train loss: 2.4169 | train accuracy: 99.838 | test loss: 1.3130 | test accuracy: 66.880 | epoch runtime:   5.57 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:52:58,206 - INFO - Evaluate Summary Time 1.73s\tLoss 1.2972\t Acc@1 68.0400\t Acc@5 95.0200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:52:58,206 - INFO - Head 85.100\tMid 60.550\tTail 60.967\u001b[0m\n",
      "\u001b[32m2024-10-06 14:52:58,206 - INFO - epoch: 148 | train loss: 2.4180 | train accuracy: 99.853 | test loss: 1.2972 | test accuracy: 68.040 | epoch runtime:   5.62 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:53:03,862 - INFO - Evaluate Summary Time 1.76s\tLoss 1.3030\t Acc@1 66.5500\t Acc@5 94.8600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:53:03,862 - INFO - Head 83.800\tMid 58.400\tTail 60.167\u001b[0m\n",
      "\u001b[32m2024-10-06 14:53:03,862 - INFO - epoch: 149 | train loss: 2.4162 | train accuracy: 99.858 | test loss: 1.3030 | test accuracy: 66.550 | epoch runtime:   5.66 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:53:09,434 - INFO - Evaluate Summary Time 1.69s\tLoss 1.3048\t Acc@1 66.3600\t Acc@5 94.7900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:53:09,434 - INFO - Head 82.633\tMid 60.075\tTail 58.467\u001b[0m\n",
      "\u001b[32m2024-10-06 14:53:09,434 - INFO - epoch: 150 | train loss: 2.4173 | train accuracy: 99.843 | test loss: 1.3048 | test accuracy: 66.360 | epoch runtime:   5.57 sec | best accuracy: 70.920 @ epoch: 024\u001b[0m\n",
      "Runtime of this script /home/zyx/zhengjinpeng/PNP/cifar.py : 835.3 seconds (0.232 hours)\n",
      "Config:\n",
      "{\n",
      "    database: Datasets\n",
      "    dataset: cifar10\n",
      "    n_classes: 10\n",
      "    rescale_size: 32\n",
      "    crop_size: 32\n",
      "    cfg_file: ./config/cifar10.cfg\n",
      "    synthetic_data: cifar80no\n",
      "    noise_type: symmetric\n",
      "    closeset_ratio: 0.2\n",
      "    r_ood: 0.2\n",
      "    r_imb: 0.01\n",
      "    gpu: 0\n",
      "    net: cnn\n",
      "    batch_size: 128\n",
      "    lr: 0.001\n",
      "    lr_decay: cosine\n",
      "    weight_decay: 1e-05\n",
      "    opt: adam\n",
      "    warmup_epochs: 5\n",
      "    warmup_lr_scale: 10.0\n",
      "    epochs: 150\n",
      "    save_model: False\n",
      "    use_fp16: False\n",
      "    use_grad_accumulate: False\n",
      "    project: \n",
      "    log: PENIOC\n",
      "    epsilon: 0.5\n",
      "    temperature: 0.1\n",
      "    eta: 0.5\n",
      "    alpha: 0.0\n",
      "    beta: 1.0\n",
      "    gamma: 1.0\n",
      "    omega: 0.1\n",
      "    rho: 1.0\n",
      "    loss_func_aux: mae\n",
      "    weighting: soft\n",
      "    neg_cons: False\n",
      "    activation: tanh\n",
      "    ablation: False\n",
      "    log_freq: 1\n",
      "    asym: False\n",
      "}\n",
      "\n",
      "Available GPUs Index : 0\n",
      "using CIFAR-10...\n",
      "Built imbalanced dataset, r_imb=0.01\n",
      "Mixing in OOD noise, r_ood=0.2\n",
      "Mixing in ID noise, r_id=0.2\n",
      "using CIFAR-10...\n",
      "\u001b[32m2024-10-06 14:53:16,096 - INFO - Categories: 10, Training Samples: 12406, Testing Samples: 10000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:53:16,096 - INFO - Optimizer: adam\u001b[0m\n",
      "\u001b[32m2024-10-06 14:53:16,097 - INFO - Accumulate gradients every 1 iterations --> Acutal batch size is 128\u001b[0m\n",
      "\u001b[32m2024-10-06 14:53:20,689 - INFO - Evaluate Summary Time 1.68s\tLoss 2.2455\t Acc@1 20.9600\t Acc@5 61.2300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:53:20,690 - INFO - Head 69.533\tMid 0.250\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:53:20,690 - INFO - epoch:   1 | train loss: 2.2968 | train accuracy: 35.112 | test loss: 2.2455 | test accuracy: 20.960 | epoch runtime:   4.59 sec | best accuracy: 20.960 @ epoch: 001\u001b[0m\n",
      "\u001b[32m2024-10-06 14:53:24,522 - INFO - Evaluate Summary Time 1.70s\tLoss 2.2030\t Acc@1 22.1200\t Acc@5 62.4200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:53:24,522 - INFO - Head 64.367\tMid 7.025\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:53:24,523 - INFO - epoch:   2 | train loss: 2.2066 | train accuracy: 41.383 | test loss: 2.2030 | test accuracy: 22.120 | epoch runtime:   3.83 sec | best accuracy: 22.120 @ epoch: 002\u001b[0m\n",
      "\u001b[32m2024-10-06 14:53:28,234 - INFO - Evaluate Summary Time 1.65s\tLoss 2.1513\t Acc@1 26.3800\t Acc@5 61.1500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:53:28,234 - INFO - Head 67.900\tMid 14.975\tTail 0.067\u001b[0m\n",
      "\u001b[32m2024-10-06 14:53:28,234 - INFO - epoch:   3 | train loss: 2.1905 | train accuracy: 44.140 | test loss: 2.1513 | test accuracy: 26.380 | epoch runtime:   3.71 sec | best accuracy: 26.380 @ epoch: 003\u001b[0m\n",
      "\u001b[32m2024-10-06 14:53:31,973 - INFO - Evaluate Summary Time 1.63s\tLoss 2.1621\t Acc@1 25.5600\t Acc@5 58.4900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:53:31,973 - INFO - Head 73.633\tMid 8.675\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:53:31,973 - INFO - epoch:   4 | train loss: 2.1772 | train accuracy: 45.220 | test loss: 2.1621 | test accuracy: 25.560 | epoch runtime:   3.74 sec | best accuracy: 26.380 @ epoch: 003\u001b[0m\n",
      "\u001b[32m2024-10-06 14:53:35,840 - INFO - Evaluate Summary Time 1.70s\tLoss 2.1457\t Acc@1 24.9500\t Acc@5 59.3900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:53:35,840 - INFO - Head 77.967\tMid 3.900\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:53:35,840 - INFO - epoch:   5 | train loss: 2.1707 | train accuracy: 47.364 | test loss: 2.1457 | test accuracy: 24.950 | epoch runtime:   3.87 sec | best accuracy: 26.380 @ epoch: 003\u001b[0m\n",
      "\u001b[32m2024-10-06 14:53:40,084 - INFO - Evaluate Summary Time 1.77s\tLoss 2.1221\t Acc@1 25.9400\t Acc@5 67.0600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:53:40,084 - INFO - Head 76.167\tMid 7.725\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:53:40,084 - INFO - epoch:   6 | train loss: 2.7563 | train accuracy: 49.379 | test loss: 2.1221 | test accuracy: 25.940 | epoch runtime:   4.24 sec | best accuracy: 26.380 @ epoch: 003\u001b[0m\n",
      "\u001b[32m2024-10-06 14:53:44,180 - INFO - Evaluate Summary Time 1.70s\tLoss 2.1135\t Acc@1 27.4800\t Acc@5 67.7400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:53:44,181 - INFO - Head 78.000\tMid 10.200\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:53:44,181 - INFO - epoch:   7 | train loss: 2.7329 | train accuracy: 49.621 | test loss: 2.1135 | test accuracy: 27.480 | epoch runtime:   4.10 sec | best accuracy: 27.480 @ epoch: 007\u001b[0m\n",
      "\u001b[32m2024-10-06 14:53:48,416 - INFO - Evaluate Summary Time 1.79s\tLoss 2.1073\t Acc@1 27.9300\t Acc@5 67.5100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:53:48,417 - INFO - Head 79.367\tMid 10.300\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:53:48,417 - INFO - epoch:   8 | train loss: 2.7263 | train accuracy: 50.137 | test loss: 2.1073 | test accuracy: 27.930 | epoch runtime:   4.24 sec | best accuracy: 27.930 @ epoch: 008\u001b[0m\n",
      "\u001b[32m2024-10-06 14:53:52,518 - INFO - Evaluate Summary Time 1.70s\tLoss 2.1064\t Acc@1 28.4700\t Acc@5 67.0300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:53:52,518 - INFO - Head 79.267\tMid 11.725\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:53:52,519 - INFO - epoch:   9 | train loss: 2.7244 | train accuracy: 50.621 | test loss: 2.1064 | test accuracy: 28.470 | epoch runtime:   4.10 sec | best accuracy: 28.470 @ epoch: 009\u001b[0m\n",
      "\u001b[32m2024-10-06 14:53:56,830 - INFO - Evaluate Summary Time 1.80s\tLoss 2.0948\t Acc@1 28.8300\t Acc@5 67.2400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:53:56,830 - INFO - Head 81.300\tMid 11.100\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:53:56,830 - INFO - epoch:  10 | train loss: 2.7248 | train accuracy: 50.540 | test loss: 2.0948 | test accuracy: 28.830 | epoch runtime:   4.31 sec | best accuracy: 28.830 @ epoch: 010\u001b[0m\n",
      "\u001b[32m2024-10-06 14:54:00,949 - INFO - Evaluate Summary Time 1.68s\tLoss 2.1102\t Acc@1 28.3200\t Acc@5 66.7900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:54:00,949 - INFO - Head 78.033\tMid 12.275\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:54:00,949 - INFO - epoch:  11 | train loss: 2.7193 | train accuracy: 50.846 | test loss: 2.1102 | test accuracy: 28.320 | epoch runtime:   4.12 sec | best accuracy: 28.830 @ epoch: 010\u001b[0m\n",
      "\u001b[32m2024-10-06 14:54:05,085 - INFO - Evaluate Summary Time 1.65s\tLoss 2.0979\t Acc@1 28.6100\t Acc@5 68.5500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:54:05,085 - INFO - Head 80.067\tMid 11.450\tTail 0.033\u001b[0m\n",
      "\u001b[32m2024-10-06 14:54:05,085 - INFO - epoch:  12 | train loss: 2.7182 | train accuracy: 51.008 | test loss: 2.0979 | test accuracy: 28.610 | epoch runtime:   4.14 sec | best accuracy: 28.830 @ epoch: 010\u001b[0m\n",
      "\u001b[32m2024-10-06 14:54:09,257 - INFO - Evaluate Summary Time 1.73s\tLoss 2.0843\t Acc@1 29.6600\t Acc@5 70.2400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:54:09,257 - INFO - Head 82.000\tMid 12.650\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:54:09,257 - INFO - epoch:  13 | train loss: 2.7173 | train accuracy: 51.266 | test loss: 2.0843 | test accuracy: 29.660 | epoch runtime:   4.17 sec | best accuracy: 29.660 @ epoch: 013\u001b[0m\n",
      "\u001b[32m2024-10-06 14:54:13,400 - INFO - Evaluate Summary Time 1.68s\tLoss 2.0954\t Acc@1 29.7600\t Acc@5 67.6400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:54:13,400 - INFO - Head 81.033\tMid 13.625\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:54:13,401 - INFO - epoch:  14 | train loss: 2.7162 | train accuracy: 51.306 | test loss: 2.0954 | test accuracy: 29.760 | epoch runtime:   4.14 sec | best accuracy: 29.760 @ epoch: 014\u001b[0m\n",
      "\u001b[32m2024-10-06 14:54:17,608 - INFO - Evaluate Summary Time 1.76s\tLoss 2.0723\t Acc@1 29.9900\t Acc@5 70.6600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:54:17,609 - INFO - Head 82.467\tMid 13.075\tTail 0.067\u001b[0m\n",
      "\u001b[32m2024-10-06 14:54:17,609 - INFO - epoch:  15 | train loss: 2.7152 | train accuracy: 51.523 | test loss: 2.0723 | test accuracy: 29.990 | epoch runtime:   4.21 sec | best accuracy: 29.990 @ epoch: 015\u001b[0m\n",
      "\u001b[32m2024-10-06 14:54:21,637 - INFO - Evaluate Summary Time 1.62s\tLoss 2.0825\t Acc@1 29.8800\t Acc@5 72.3600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:54:21,637 - INFO - Head 80.333\tMid 14.450\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:54:21,637 - INFO - epoch:  16 | train loss: 2.7136 | train accuracy: 51.838 | test loss: 2.0825 | test accuracy: 29.880 | epoch runtime:   4.03 sec | best accuracy: 29.990 @ epoch: 015\u001b[0m\n",
      "\u001b[32m2024-10-06 14:54:25,863 - INFO - Evaluate Summary Time 1.78s\tLoss 2.0805\t Acc@1 29.4800\t Acc@5 70.4800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:54:25,864 - INFO - Head 80.367\tMid 13.425\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:54:25,864 - INFO - epoch:  17 | train loss: 2.7117 | train accuracy: 52.402 | test loss: 2.0805 | test accuracy: 29.480 | epoch runtime:   4.23 sec | best accuracy: 29.990 @ epoch: 015\u001b[0m\n",
      "\u001b[32m2024-10-06 14:54:29,967 - INFO - Evaluate Summary Time 1.70s\tLoss 2.0769\t Acc@1 30.5800\t Acc@5 72.2400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:54:29,967 - INFO - Head 79.933\tMid 16.500\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:54:29,967 - INFO - epoch:  18 | train loss: 2.7103 | train accuracy: 52.499 | test loss: 2.0769 | test accuracy: 30.580 | epoch runtime:   4.10 sec | best accuracy: 30.580 @ epoch: 018\u001b[0m\n",
      "\u001b[32m2024-10-06 14:54:34,156 - INFO - Evaluate Summary Time 1.75s\tLoss 2.0750\t Acc@1 30.6900\t Acc@5 69.5300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:54:34,156 - INFO - Head 83.900\tMid 13.800\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:54:34,156 - INFO - epoch:  19 | train loss: 2.7085 | train accuracy: 52.990 | test loss: 2.0750 | test accuracy: 30.690 | epoch runtime:   4.19 sec | best accuracy: 30.690 @ epoch: 019\u001b[0m\n",
      "\u001b[32m2024-10-06 14:54:38,381 - INFO - Evaluate Summary Time 1.70s\tLoss 2.0514\t Acc@1 31.5500\t Acc@5 71.9800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:54:38,382 - INFO - Head 81.833\tMid 17.500\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:54:38,382 - INFO - epoch:  20 | train loss: 2.7069 | train accuracy: 53.265 | test loss: 2.0514 | test accuracy: 31.550 | epoch runtime:   4.23 sec | best accuracy: 31.550 @ epoch: 020\u001b[0m\n",
      "\u001b[32m2024-10-06 14:54:42,478 - INFO - Evaluate Summary Time 1.68s\tLoss 2.0590\t Acc@1 31.1900\t Acc@5 71.8800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:54:42,478 - INFO - Head 84.433\tMid 14.650\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:54:42,478 - INFO - epoch:  21 | train loss: 2.7048 | train accuracy: 53.853 | test loss: 2.0590 | test accuracy: 31.190 | epoch runtime:   4.10 sec | best accuracy: 31.550 @ epoch: 020\u001b[0m\n",
      "\u001b[32m2024-10-06 14:54:46,702 - INFO - Evaluate Summary Time 1.73s\tLoss 2.0454\t Acc@1 30.7800\t Acc@5 75.0300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:54:46,703 - INFO - Head 86.800\tMid 11.850\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:54:46,703 - INFO - epoch:  22 | train loss: 2.7034 | train accuracy: 54.071 | test loss: 2.0454 | test accuracy: 30.780 | epoch runtime:   4.22 sec | best accuracy: 31.550 @ epoch: 020\u001b[0m\n",
      "\u001b[32m2024-10-06 14:54:50,763 - INFO - Evaluate Summary Time 1.68s\tLoss 2.0349\t Acc@1 32.2600\t Acc@5 75.3000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:54:50,763 - INFO - Head 81.700\tMid 19.375\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:54:50,764 - INFO - epoch:  23 | train loss: 2.7029 | train accuracy: 54.691 | test loss: 2.0349 | test accuracy: 32.260 | epoch runtime:   4.06 sec | best accuracy: 32.260 @ epoch: 023\u001b[0m\n",
      "\u001b[32m2024-10-06 14:54:54,829 - INFO - Evaluate Summary Time 1.64s\tLoss 2.0260\t Acc@1 33.4300\t Acc@5 74.6100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:54:54,830 - INFO - Head 83.400\tMid 21.025\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:54:54,830 - INFO - epoch:  24 | train loss: 2.7016 | train accuracy: 54.861 | test loss: 2.0260 | test accuracy: 33.430 | epoch runtime:   4.07 sec | best accuracy: 33.430 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:54:58,936 - INFO - Evaluate Summary Time 1.69s\tLoss 2.0259\t Acc@1 32.8600\t Acc@5 75.5300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:54:58,937 - INFO - Head 82.933\tMid 19.950\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:54:58,937 - INFO - epoch:  25 | train loss: 2.6981 | train accuracy: 55.239 | test loss: 2.0259 | test accuracy: 32.860 | epoch runtime:   4.11 sec | best accuracy: 33.430 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:55:03,067 - INFO - Evaluate Summary Time 1.69s\tLoss 2.0271\t Acc@1 32.5400\t Acc@5 77.4900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:55:03,067 - INFO - Head 81.633\tMid 20.125\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:55:03,067 - INFO - epoch:  26 | train loss: 2.6963 | train accuracy: 55.755 | test loss: 2.0271 | test accuracy: 32.540 | epoch runtime:   4.13 sec | best accuracy: 33.430 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:55:07,073 - INFO - Evaluate Summary Time 1.63s\tLoss 2.0367\t Acc@1 32.2700\t Acc@5 77.0400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:55:07,074 - INFO - Head 84.567\tMid 17.250\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:55:07,074 - INFO - epoch:  27 | train loss: 2.6931 | train accuracy: 56.561 | test loss: 2.0367 | test accuracy: 32.270 | epoch runtime:   4.01 sec | best accuracy: 33.430 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 14:55:11,122 - INFO - Evaluate Summary Time 1.62s\tLoss 2.0194\t Acc@1 33.7800\t Acc@5 78.5300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:55:11,123 - INFO - Head 84.067\tMid 21.400\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:55:11,123 - INFO - epoch:  28 | train loss: 2.6931 | train accuracy: 56.400 | test loss: 2.0194 | test accuracy: 33.780 | epoch runtime:   4.05 sec | best accuracy: 33.780 @ epoch: 028\u001b[0m\n",
      "\u001b[32m2024-10-06 14:55:15,438 - INFO - Evaluate Summary Time 1.82s\tLoss 1.9923\t Acc@1 35.1300\t Acc@5 80.5000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:55:15,438 - INFO - Head 81.267\tMid 26.850\tTail 0.033\u001b[0m\n",
      "\u001b[32m2024-10-06 14:55:15,438 - INFO - epoch:  29 | train loss: 2.6899 | train accuracy: 57.230 | test loss: 1.9923 | test accuracy: 35.130 | epoch runtime:   4.32 sec | best accuracy: 35.130 @ epoch: 029\u001b[0m\n",
      "\u001b[32m2024-10-06 14:55:19,519 - INFO - Evaluate Summary Time 1.70s\tLoss 2.0104\t Acc@1 35.1200\t Acc@5 74.3800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:55:19,519 - INFO - Head 84.800\tMid 24.200\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:55:19,519 - INFO - epoch:  30 | train loss: 2.6877 | train accuracy: 57.988 | test loss: 2.0104 | test accuracy: 35.120 | epoch runtime:   4.08 sec | best accuracy: 35.130 @ epoch: 029\u001b[0m\n",
      "\u001b[32m2024-10-06 14:55:23,763 - INFO - Evaluate Summary Time 1.78s\tLoss 1.9915\t Acc@1 35.6000\t Acc@5 79.1000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:55:23,764 - INFO - Head 82.633\tMid 27.000\tTail 0.033\u001b[0m\n",
      "\u001b[32m2024-10-06 14:55:23,764 - INFO - epoch:  31 | train loss: 2.6846 | train accuracy: 58.182 | test loss: 1.9915 | test accuracy: 35.600 | epoch runtime:   4.24 sec | best accuracy: 35.600 @ epoch: 031\u001b[0m\n",
      "\u001b[32m2024-10-06 14:55:27,910 - INFO - Evaluate Summary Time 1.72s\tLoss 1.9750\t Acc@1 33.2600\t Acc@5 81.7500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:55:27,910 - INFO - Head 87.067\tMid 17.775\tTail 0.100\u001b[0m\n",
      "\u001b[32m2024-10-06 14:55:27,911 - INFO - epoch:  32 | train loss: 2.6826 | train accuracy: 58.641 | test loss: 1.9750 | test accuracy: 33.260 | epoch runtime:   4.15 sec | best accuracy: 35.600 @ epoch: 031\u001b[0m\n",
      "\u001b[32m2024-10-06 14:55:32,028 - INFO - Evaluate Summary Time 1.70s\tLoss 1.9689\t Acc@1 37.1100\t Acc@5 82.6300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:55:32,029 - INFO - Head 81.400\tMid 31.675\tTail 0.067\u001b[0m\n",
      "\u001b[32m2024-10-06 14:55:32,029 - INFO - epoch:  33 | train loss: 2.6799 | train accuracy: 59.423 | test loss: 1.9689 | test accuracy: 37.110 | epoch runtime:   4.12 sec | best accuracy: 37.110 @ epoch: 033\u001b[0m\n",
      "\u001b[32m2024-10-06 14:55:36,224 - INFO - Evaluate Summary Time 1.77s\tLoss 1.9776\t Acc@1 35.2700\t Acc@5 80.8900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:55:36,224 - INFO - Head 82.067\tMid 26.625\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:55:36,224 - INFO - epoch:  34 | train loss: 2.6785 | train accuracy: 59.842 | test loss: 1.9776 | test accuracy: 35.270 | epoch runtime:   4.19 sec | best accuracy: 37.110 @ epoch: 033\u001b[0m\n",
      "\u001b[32m2024-10-06 14:55:40,386 - INFO - Evaluate Summary Time 1.74s\tLoss 1.9772\t Acc@1 36.9700\t Acc@5 80.7900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:55:40,387 - INFO - Head 82.533\tMid 30.350\tTail 0.233\u001b[0m\n",
      "\u001b[32m2024-10-06 14:55:40,387 - INFO - epoch:  35 | train loss: 2.6766 | train accuracy: 60.132 | test loss: 1.9772 | test accuracy: 36.970 | epoch runtime:   4.16 sec | best accuracy: 37.110 @ epoch: 033\u001b[0m\n",
      "\u001b[32m2024-10-06 14:55:44,475 - INFO - Evaluate Summary Time 1.67s\tLoss 1.9469\t Acc@1 37.8600\t Acc@5 84.9200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:55:44,475 - INFO - Head 83.900\tMid 31.400\tTail 0.433\u001b[0m\n",
      "\u001b[32m2024-10-06 14:55:44,476 - INFO - epoch:  36 | train loss: 2.6747 | train accuracy: 60.632 | test loss: 1.9469 | test accuracy: 37.860 | epoch runtime:   4.09 sec | best accuracy: 37.860 @ epoch: 036\u001b[0m\n",
      "\u001b[32m2024-10-06 14:55:48,599 - INFO - Evaluate Summary Time 1.66s\tLoss 1.9435\t Acc@1 37.9000\t Acc@5 81.0100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:55:48,600 - INFO - Head 84.567\tMid 30.825\tTail 0.667\u001b[0m\n",
      "\u001b[32m2024-10-06 14:55:48,600 - INFO - epoch:  37 | train loss: 2.6704 | train accuracy: 61.293 | test loss: 1.9435 | test accuracy: 37.900 | epoch runtime:   4.12 sec | best accuracy: 37.900 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 14:55:52,783 - INFO - Evaluate Summary Time 1.77s\tLoss 1.9594\t Acc@1 37.0400\t Acc@5 80.8800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:55:52,783 - INFO - Head 81.800\tMid 30.475\tTail 1.033\u001b[0m\n",
      "\u001b[32m2024-10-06 14:55:52,784 - INFO - epoch:  38 | train loss: 2.6698 | train accuracy: 62.091 | test loss: 1.9594 | test accuracy: 37.040 | epoch runtime:   4.18 sec | best accuracy: 37.900 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 14:55:57,012 - INFO - Evaluate Summary Time 1.79s\tLoss 1.9366\t Acc@1 39.0100\t Acc@5 83.1200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:55:57,012 - INFO - Head 82.900\tMid 34.450\tTail 1.200\u001b[0m\n",
      "\u001b[32m2024-10-06 14:55:57,013 - INFO - epoch:  39 | train loss: 2.6663 | train accuracy: 62.728 | test loss: 1.9366 | test accuracy: 39.010 | epoch runtime:   4.23 sec | best accuracy: 39.010 @ epoch: 039\u001b[0m\n",
      "\u001b[32m2024-10-06 14:56:01,131 - INFO - Evaluate Summary Time 1.66s\tLoss 1.9388\t Acc@1 38.4800\t Acc@5 80.6000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:56:01,132 - INFO - Head 84.000\tMid 32.750\tTail 0.600\u001b[0m\n",
      "\u001b[32m2024-10-06 14:56:01,132 - INFO - epoch:  40 | train loss: 2.6634 | train accuracy: 63.477 | test loss: 1.9388 | test accuracy: 38.480 | epoch runtime:   4.12 sec | best accuracy: 39.010 @ epoch: 039\u001b[0m\n",
      "\u001b[32m2024-10-06 14:56:05,351 - INFO - Evaluate Summary Time 1.78s\tLoss 1.9593\t Acc@1 37.6900\t Acc@5 82.7800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:56:05,351 - INFO - Head 81.000\tMid 32.050\tTail 1.900\u001b[0m\n",
      "\u001b[32m2024-10-06 14:56:05,352 - INFO - epoch:  41 | train loss: 2.6618 | train accuracy: 64.227 | test loss: 1.9593 | test accuracy: 37.690 | epoch runtime:   4.22 sec | best accuracy: 39.010 @ epoch: 039\u001b[0m\n",
      "\u001b[32m2024-10-06 14:56:09,364 - INFO - Evaluate Summary Time 1.66s\tLoss 1.9191\t Acc@1 40.7500\t Acc@5 82.8200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:56:09,365 - INFO - Head 81.000\tMid 40.375\tTail 1.000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:56:09,365 - INFO - epoch:  42 | train loss: 2.6594 | train accuracy: 64.662 | test loss: 1.9191 | test accuracy: 40.750 | epoch runtime:   4.01 sec | best accuracy: 40.750 @ epoch: 042\u001b[0m\n",
      "\u001b[32m2024-10-06 14:56:13,488 - INFO - Evaluate Summary Time 1.74s\tLoss 1.8925\t Acc@1 41.3000\t Acc@5 86.8000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:56:13,489 - INFO - Head 81.833\tMid 39.100\tTail 3.700\u001b[0m\n",
      "\u001b[32m2024-10-06 14:56:13,489 - INFO - epoch:  43 | train loss: 2.6579 | train accuracy: 65.589 | test loss: 1.8925 | test accuracy: 41.300 | epoch runtime:   4.12 sec | best accuracy: 41.300 @ epoch: 043\u001b[0m\n",
      "\u001b[32m2024-10-06 14:56:17,706 - INFO - Evaluate Summary Time 1.80s\tLoss 1.9480\t Acc@1 38.7000\t Acc@5 82.2200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:56:17,707 - INFO - Head 82.733\tMid 31.500\tTail 4.267\u001b[0m\n",
      "\u001b[32m2024-10-06 14:56:17,707 - INFO - epoch:  44 | train loss: 2.6541 | train accuracy: 66.621 | test loss: 1.9480 | test accuracy: 38.700 | epoch runtime:   4.22 sec | best accuracy: 41.300 @ epoch: 043\u001b[0m\n",
      "\u001b[32m2024-10-06 14:56:21,851 - INFO - Evaluate Summary Time 1.73s\tLoss 1.9301\t Acc@1 39.3200\t Acc@5 82.4200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:56:21,851 - INFO - Head 82.333\tMid 33.750\tTail 3.733\u001b[0m\n",
      "\u001b[32m2024-10-06 14:56:21,851 - INFO - epoch:  45 | train loss: 2.6511 | train accuracy: 67.201 | test loss: 1.9301 | test accuracy: 39.320 | epoch runtime:   4.14 sec | best accuracy: 41.300 @ epoch: 043\u001b[0m\n",
      "\u001b[32m2024-10-06 14:56:26,004 - INFO - Evaluate Summary Time 1.69s\tLoss 1.8615\t Acc@1 41.4000\t Acc@5 84.4300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:56:26,005 - INFO - Head 82.533\tMid 39.000\tTail 3.467\u001b[0m\n",
      "\u001b[32m2024-10-06 14:56:26,005 - INFO - epoch:  46 | train loss: 2.6490 | train accuracy: 67.943 | test loss: 1.8615 | test accuracy: 41.400 | epoch runtime:   4.15 sec | best accuracy: 41.400 @ epoch: 046\u001b[0m\n",
      "\u001b[32m2024-10-06 14:56:30,145 - INFO - Evaluate Summary Time 1.71s\tLoss 1.8903\t Acc@1 40.3400\t Acc@5 84.2800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:56:30,146 - INFO - Head 82.233\tMid 36.825\tTail 3.133\u001b[0m\n",
      "\u001b[32m2024-10-06 14:56:30,146 - INFO - epoch:  47 | train loss: 2.6428 | train accuracy: 68.733 | test loss: 1.8903 | test accuracy: 40.340 | epoch runtime:   4.14 sec | best accuracy: 41.400 @ epoch: 046\u001b[0m\n",
      "\u001b[32m2024-10-06 14:56:34,260 - INFO - Evaluate Summary Time 1.73s\tLoss 1.8918\t Acc@1 40.6500\t Acc@5 82.4800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:56:34,260 - INFO - Head 85.800\tMid 34.850\tTail 3.233\u001b[0m\n",
      "\u001b[32m2024-10-06 14:56:34,260 - INFO - epoch:  48 | train loss: 2.6378 | train accuracy: 69.563 | test loss: 1.8918 | test accuracy: 40.650 | epoch runtime:   4.11 sec | best accuracy: 41.400 @ epoch: 046\u001b[0m\n",
      "\u001b[32m2024-10-06 14:56:38,544 - INFO - Evaluate Summary Time 1.78s\tLoss 1.8513\t Acc@1 41.8100\t Acc@5 82.9300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:56:38,544 - INFO - Head 81.833\tMid 39.025\tTail 5.500\u001b[0m\n",
      "\u001b[32m2024-10-06 14:56:38,545 - INFO - epoch:  49 | train loss: 2.6324 | train accuracy: 70.796 | test loss: 1.8513 | test accuracy: 41.810 | epoch runtime:   4.28 sec | best accuracy: 41.810 @ epoch: 049\u001b[0m\n",
      "\u001b[32m2024-10-06 14:56:42,826 - INFO - Evaluate Summary Time 1.73s\tLoss 1.8331\t Acc@1 42.8500\t Acc@5 84.0600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:56:42,826 - INFO - Head 83.033\tMid 39.775\tTail 6.767\u001b[0m\n",
      "\u001b[32m2024-10-06 14:56:42,827 - INFO - epoch:  50 | train loss: 2.6269 | train accuracy: 71.941 | test loss: 1.8331 | test accuracy: 42.850 | epoch runtime:   4.28 sec | best accuracy: 42.850 @ epoch: 050\u001b[0m\n",
      "\u001b[32m2024-10-06 14:56:46,967 - INFO - Evaluate Summary Time 1.70s\tLoss 1.8581\t Acc@1 41.9700\t Acc@5 82.0800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:56:46,968 - INFO - Head 82.200\tMid 39.275\tTail 5.333\u001b[0m\n",
      "\u001b[32m2024-10-06 14:56:46,968 - INFO - epoch:  51 | train loss: 2.6257 | train accuracy: 72.997 | test loss: 1.8581 | test accuracy: 41.970 | epoch runtime:   4.14 sec | best accuracy: 42.850 @ epoch: 050\u001b[0m\n",
      "\u001b[32m2024-10-06 14:56:51,111 - INFO - Evaluate Summary Time 1.72s\tLoss 1.8659\t Acc@1 42.0000\t Acc@5 83.1300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:56:51,111 - INFO - Head 81.700\tMid 40.675\tTail 4.067\u001b[0m\n",
      "\u001b[32m2024-10-06 14:56:51,112 - INFO - epoch:  52 | train loss: 2.6190 | train accuracy: 74.649 | test loss: 1.8659 | test accuracy: 42.000 | epoch runtime:   4.14 sec | best accuracy: 42.850 @ epoch: 050\u001b[0m\n",
      "\u001b[32m2024-10-06 14:56:55,260 - INFO - Evaluate Summary Time 1.72s\tLoss 1.8675\t Acc@1 41.0200\t Acc@5 83.1200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:56:55,261 - INFO - Head 83.367\tMid 35.075\tTail 6.600\u001b[0m\n",
      "\u001b[32m2024-10-06 14:56:55,261 - INFO - epoch:  53 | train loss: 2.6151 | train accuracy: 75.770 | test loss: 1.8675 | test accuracy: 41.020 | epoch runtime:   4.15 sec | best accuracy: 42.850 @ epoch: 050\u001b[0m\n",
      "\u001b[32m2024-10-06 14:56:59,420 - INFO - Evaluate Summary Time 1.73s\tLoss 1.8586\t Acc@1 42.5500\t Acc@5 82.3000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:56:59,420 - INFO - Head 81.833\tMid 40.475\tTail 6.033\u001b[0m\n",
      "\u001b[32m2024-10-06 14:56:59,420 - INFO - epoch:  54 | train loss: 2.6110 | train accuracy: 76.777 | test loss: 1.8586 | test accuracy: 42.550 | epoch runtime:   4.16 sec | best accuracy: 42.850 @ epoch: 050\u001b[0m\n",
      "\u001b[32m2024-10-06 14:57:03,530 - INFO - Evaluate Summary Time 1.73s\tLoss 1.8286\t Acc@1 43.7200\t Acc@5 85.2100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:57:03,530 - INFO - Head 80.800\tMid 42.475\tTail 8.300\u001b[0m\n",
      "\u001b[32m2024-10-06 14:57:03,531 - INFO - epoch:  55 | train loss: 2.6082 | train accuracy: 77.930 | test loss: 1.8286 | test accuracy: 43.720 | epoch runtime:   4.11 sec | best accuracy: 43.720 @ epoch: 055\u001b[0m\n",
      "\u001b[32m2024-10-06 14:57:07,618 - INFO - Evaluate Summary Time 1.68s\tLoss 1.8407\t Acc@1 41.9200\t Acc@5 82.1100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:57:07,618 - INFO - Head 81.067\tMid 36.550\tTail 9.933\u001b[0m\n",
      "\u001b[32m2024-10-06 14:57:07,618 - INFO - epoch:  56 | train loss: 2.6027 | train accuracy: 79.494 | test loss: 1.8407 | test accuracy: 41.920 | epoch runtime:   4.09 sec | best accuracy: 43.720 @ epoch: 055\u001b[0m\n",
      "\u001b[32m2024-10-06 14:57:11,750 - INFO - Evaluate Summary Time 1.72s\tLoss 1.8008\t Acc@1 44.5500\t Acc@5 85.0400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:57:11,751 - INFO - Head 78.900\tMid 44.875\tTail 9.767\u001b[0m\n",
      "\u001b[32m2024-10-06 14:57:11,751 - INFO - epoch:  57 | train loss: 2.5974 | train accuracy: 80.284 | test loss: 1.8008 | test accuracy: 44.550 | epoch runtime:   4.13 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 14:57:15,843 - INFO - Evaluate Summary Time 1.65s\tLoss 1.8246\t Acc@1 42.7400\t Acc@5 84.1300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:57:15,843 - INFO - Head 77.333\tMid 42.650\tTail 8.267\u001b[0m\n",
      "\u001b[32m2024-10-06 14:57:15,844 - INFO - epoch:  58 | train loss: 2.5936 | train accuracy: 81.654 | test loss: 1.8246 | test accuracy: 42.740 | epoch runtime:   4.09 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 14:57:19,980 - INFO - Evaluate Summary Time 1.70s\tLoss 1.8556\t Acc@1 41.3800\t Acc@5 82.0200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:57:19,981 - INFO - Head 80.633\tMid 36.925\tTail 8.067\u001b[0m\n",
      "\u001b[32m2024-10-06 14:57:19,981 - INFO - epoch:  59 | train loss: 2.5903 | train accuracy: 82.629 | test loss: 1.8556 | test accuracy: 41.380 | epoch runtime:   4.14 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 14:57:24,085 - INFO - Evaluate Summary Time 1.69s\tLoss 1.8352\t Acc@1 42.2600\t Acc@5 82.0300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:57:24,085 - INFO - Head 82.667\tMid 37.800\tTail 7.800\u001b[0m\n",
      "\u001b[32m2024-10-06 14:57:24,085 - INFO - epoch:  60 | train loss: 2.5854 | train accuracy: 84.056 | test loss: 1.8352 | test accuracy: 42.260 | epoch runtime:   4.10 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 14:57:28,238 - INFO - Evaluate Summary Time 1.72s\tLoss 1.8308\t Acc@1 43.8100\t Acc@5 81.5700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:57:28,238 - INFO - Head 82.033\tMid 40.975\tTail 9.367\u001b[0m\n",
      "\u001b[32m2024-10-06 14:57:28,238 - INFO - epoch:  61 | train loss: 2.5803 | train accuracy: 85.080 | test loss: 1.8308 | test accuracy: 43.810 | epoch runtime:   4.15 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 14:57:32,401 - INFO - Evaluate Summary Time 1.73s\tLoss 1.8578\t Acc@1 41.4200\t Acc@5 81.2600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:57:32,402 - INFO - Head 79.600\tMid 37.900\tTail 7.933\u001b[0m\n",
      "\u001b[32m2024-10-06 14:57:32,402 - INFO - epoch:  62 | train loss: 2.5782 | train accuracy: 85.862 | test loss: 1.8578 | test accuracy: 41.420 | epoch runtime:   4.16 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 14:57:36,662 - INFO - Evaluate Summary Time 1.78s\tLoss 1.8735\t Acc@1 41.1100\t Acc@5 81.2000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:57:36,663 - INFO - Head 80.767\tMid 35.875\tTail 8.433\u001b[0m\n",
      "\u001b[32m2024-10-06 14:57:36,663 - INFO - epoch:  63 | train loss: 2.5759 | train accuracy: 86.893 | test loss: 1.8735 | test accuracy: 41.110 | epoch runtime:   4.26 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 14:57:40,894 - INFO - Evaluate Summary Time 1.72s\tLoss 1.8212\t Acc@1 43.0400\t Acc@5 82.6400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:57:40,895 - INFO - Head 77.800\tMid 40.400\tTail 11.800\u001b[0m\n",
      "\u001b[32m2024-10-06 14:57:40,895 - INFO - epoch:  64 | train loss: 2.5719 | train accuracy: 87.925 | test loss: 1.8212 | test accuracy: 43.040 | epoch runtime:   4.23 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 14:57:44,944 - INFO - Evaluate Summary Time 1.65s\tLoss 1.8155\t Acc@1 44.4300\t Acc@5 82.4000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:57:44,944 - INFO - Head 80.500\tMid 42.950\tTail 10.333\u001b[0m\n",
      "\u001b[32m2024-10-06 14:57:44,944 - INFO - epoch:  65 | train loss: 2.5682 | train accuracy: 89.078 | test loss: 1.8155 | test accuracy: 44.430 | epoch runtime:   4.05 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 14:57:49,119 - INFO - Evaluate Summary Time 1.78s\tLoss 1.7825\t Acc@1 44.4800\t Acc@5 84.2500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:57:49,119 - INFO - Head 75.800\tMid 44.850\tTail 12.667\u001b[0m\n",
      "\u001b[32m2024-10-06 14:57:49,119 - INFO - epoch:  66 | train loss: 2.5623 | train accuracy: 89.674 | test loss: 1.7825 | test accuracy: 44.480 | epoch runtime:   4.17 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 14:57:53,293 - INFO - Evaluate Summary Time 1.73s\tLoss 1.8122\t Acc@1 43.4400\t Acc@5 82.2000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:57:53,293 - INFO - Head 81.367\tMid 39.025\tTail 11.400\u001b[0m\n",
      "\u001b[32m2024-10-06 14:57:53,293 - INFO - epoch:  67 | train loss: 2.5625 | train accuracy: 90.231 | test loss: 1.8122 | test accuracy: 43.440 | epoch runtime:   4.17 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 14:57:57,441 - INFO - Evaluate Summary Time 1.69s\tLoss 1.8654\t Acc@1 41.5700\t Acc@5 79.9700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:57:57,442 - INFO - Head 80.033\tMid 37.000\tTail 9.200\u001b[0m\n",
      "\u001b[32m2024-10-06 14:57:57,442 - INFO - epoch:  68 | train loss: 2.5580 | train accuracy: 91.214 | test loss: 1.8654 | test accuracy: 41.570 | epoch runtime:   4.15 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 14:58:01,532 - INFO - Evaluate Summary Time 1.67s\tLoss 1.8501\t Acc@1 42.3500\t Acc@5 81.1800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:58:01,533 - INFO - Head 79.233\tMid 37.200\tTail 12.333\u001b[0m\n",
      "\u001b[32m2024-10-06 14:58:01,533 - INFO - epoch:  69 | train loss: 2.5555 | train accuracy: 91.875 | test loss: 1.8501 | test accuracy: 42.350 | epoch runtime:   4.09 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 14:58:05,633 - INFO - Evaluate Summary Time 1.64s\tLoss 1.8280\t Acc@1 42.7800\t Acc@5 82.0400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:58:05,633 - INFO - Head 79.933\tMid 39.375\tTail 10.167\u001b[0m\n",
      "\u001b[32m2024-10-06 14:58:05,634 - INFO - epoch:  70 | train loss: 2.5516 | train accuracy: 92.350 | test loss: 1.8280 | test accuracy: 42.780 | epoch runtime:   4.10 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 14:58:09,731 - INFO - Evaluate Summary Time 1.64s\tLoss 1.8318\t Acc@1 43.2500\t Acc@5 82.3100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:58:09,731 - INFO - Head 77.667\tMid 41.625\tTail 11.000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:58:09,731 - INFO - epoch:  71 | train loss: 2.5492 | train accuracy: 92.995 | test loss: 1.8318 | test accuracy: 43.250 | epoch runtime:   4.10 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 14:58:13,860 - INFO - Evaluate Summary Time 1.69s\tLoss 1.8260\t Acc@1 43.2700\t Acc@5 81.4100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:58:13,860 - INFO - Head 78.633\tMid 41.250\tTail 10.600\u001b[0m\n",
      "\u001b[32m2024-10-06 14:58:13,860 - INFO - epoch:  72 | train loss: 2.5456 | train accuracy: 93.576 | test loss: 1.8260 | test accuracy: 43.270 | epoch runtime:   4.13 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 14:58:18,049 - INFO - Evaluate Summary Time 1.75s\tLoss 1.8296\t Acc@1 42.8200\t Acc@5 81.6900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:58:18,049 - INFO - Head 78.500\tMid 39.925\tTail 11.000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:58:18,050 - INFO - epoch:  73 | train loss: 2.5447 | train accuracy: 93.979 | test loss: 1.8296 | test accuracy: 42.820 | epoch runtime:   4.19 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 14:58:22,079 - INFO - Evaluate Summary Time 1.63s\tLoss 1.8618\t Acc@1 41.4200\t Acc@5 79.7900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:58:22,079 - INFO - Head 78.933\tMid 37.375\tTail 9.300\u001b[0m\n",
      "\u001b[32m2024-10-06 14:58:22,079 - INFO - epoch:  74 | train loss: 2.5419 | train accuracy: 94.511 | test loss: 1.8618 | test accuracy: 41.420 | epoch runtime:   4.03 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 14:58:26,389 - INFO - Evaluate Summary Time 1.86s\tLoss 1.8372\t Acc@1 42.6400\t Acc@5 81.9900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:58:26,390 - INFO - Head 78.267\tMid 39.050\tTail 11.800\u001b[0m\n",
      "\u001b[32m2024-10-06 14:58:26,390 - INFO - epoch:  75 | train loss: 2.5403 | train accuracy: 94.922 | test loss: 1.8372 | test accuracy: 42.640 | epoch runtime:   4.31 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 14:58:30,552 - INFO - Evaluate Summary Time 1.71s\tLoss 1.8413\t Acc@1 41.6900\t Acc@5 81.1500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:58:30,553 - INFO - Head 78.767\tMid 37.975\tTail 9.567\u001b[0m\n",
      "\u001b[32m2024-10-06 14:58:30,553 - INFO - epoch:  76 | train loss: 2.5368 | train accuracy: 95.180 | test loss: 1.8413 | test accuracy: 41.690 | epoch runtime:   4.16 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 14:58:34,668 - INFO - Evaluate Summary Time 1.74s\tLoss 1.8304\t Acc@1 42.5600\t Acc@5 80.7600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:58:34,668 - INFO - Head 80.833\tMid 36.925\tTail 11.800\u001b[0m\n",
      "\u001b[32m2024-10-06 14:58:34,669 - INFO - epoch:  77 | train loss: 2.5338 | train accuracy: 95.647 | test loss: 1.8304 | test accuracy: 42.560 | epoch runtime:   4.12 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 14:58:38,993 - INFO - Evaluate Summary Time 1.78s\tLoss 1.8310\t Acc@1 42.8500\t Acc@5 81.0700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:58:38,994 - INFO - Head 75.867\tMid 41.400\tTail 11.767\u001b[0m\n",
      "\u001b[32m2024-10-06 14:58:38,994 - INFO - epoch:  78 | train loss: 2.5341 | train accuracy: 96.026 | test loss: 1.8310 | test accuracy: 42.850 | epoch runtime:   4.33 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 14:58:43,312 - INFO - Evaluate Summary Time 1.74s\tLoss 1.8047\t Acc@1 44.4200\t Acc@5 82.1500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:58:43,313 - INFO - Head 77.300\tMid 43.775\tTail 12.400\u001b[0m\n",
      "\u001b[32m2024-10-06 14:58:43,313 - INFO - epoch:  79 | train loss: 2.5314 | train accuracy: 96.316 | test loss: 1.8047 | test accuracy: 44.420 | epoch runtime:   4.32 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 14:58:47,421 - INFO - Evaluate Summary Time 1.69s\tLoss 1.8452\t Acc@1 42.3100\t Acc@5 80.7700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:58:47,421 - INFO - Head 77.000\tMid 38.275\tTail 13.000\u001b[0m\n",
      "\u001b[32m2024-10-06 14:58:47,421 - INFO - epoch:  80 | train loss: 2.5290 | train accuracy: 96.671 | test loss: 1.8452 | test accuracy: 42.310 | epoch runtime:   4.11 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 14:58:51,588 - INFO - Evaluate Summary Time 1.78s\tLoss 1.8262\t Acc@1 43.5800\t Acc@5 81.0100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:58:51,589 - INFO - Head 78.267\tMid 40.250\tTail 13.333\u001b[0m\n",
      "\u001b[32m2024-10-06 14:58:51,589 - INFO - epoch:  81 | train loss: 2.5151 | train accuracy: 97.670 | test loss: 1.8262 | test accuracy: 43.580 | epoch runtime:   4.17 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 14:58:55,684 - INFO - Evaluate Summary Time 1.65s\tLoss 1.8327\t Acc@1 43.4200\t Acc@5 80.3500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:58:55,684 - INFO - Head 76.833\tMid 40.825\tTail 13.467\u001b[0m\n",
      "\u001b[32m2024-10-06 14:58:55,684 - INFO - epoch:  82 | train loss: 2.5100 | train accuracy: 98.356 | test loss: 1.8327 | test accuracy: 43.420 | epoch runtime:   4.09 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 14:58:59,882 - INFO - Evaluate Summary Time 1.77s\tLoss 1.8331\t Acc@1 43.3100\t Acc@5 81.0400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:58:59,883 - INFO - Head 76.900\tMid 39.575\tTail 14.700\u001b[0m\n",
      "\u001b[32m2024-10-06 14:58:59,883 - INFO - epoch:  83 | train loss: 2.5072 | train accuracy: 98.420 | test loss: 1.8331 | test accuracy: 43.310 | epoch runtime:   4.20 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 14:59:03,935 - INFO - Evaluate Summary Time 1.73s\tLoss 1.8452\t Acc@1 42.7700\t Acc@5 80.2800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:59:03,935 - INFO - Head 79.433\tMid 38.125\tTail 12.300\u001b[0m\n",
      "\u001b[32m2024-10-06 14:59:03,936 - INFO - epoch:  84 | train loss: 2.5062 | train accuracy: 98.509 | test loss: 1.8452 | test accuracy: 42.770 | epoch runtime:   4.05 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 14:59:08,052 - INFO - Evaluate Summary Time 1.68s\tLoss 1.8348\t Acc@1 43.1700\t Acc@5 80.4400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:59:08,052 - INFO - Head 78.733\tMid 40.275\tTail 11.467\u001b[0m\n",
      "\u001b[32m2024-10-06 14:59:08,053 - INFO - epoch:  85 | train loss: 2.5061 | train accuracy: 98.767 | test loss: 1.8348 | test accuracy: 43.170 | epoch runtime:   4.12 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 14:59:12,096 - INFO - Evaluate Summary Time 1.69s\tLoss 1.8436\t Acc@1 42.3800\t Acc@5 80.0000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:59:12,096 - INFO - Head 77.767\tMid 37.675\tTail 13.267\u001b[0m\n",
      "\u001b[32m2024-10-06 14:59:12,097 - INFO - epoch:  86 | train loss: 2.5038 | train accuracy: 98.734 | test loss: 1.8436 | test accuracy: 42.380 | epoch runtime:   4.04 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 14:59:16,172 - INFO - Evaluate Summary Time 1.60s\tLoss 1.8263\t Acc@1 43.8900\t Acc@5 80.7000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:59:16,172 - INFO - Head 77.533\tMid 40.275\tTail 15.067\u001b[0m\n",
      "\u001b[32m2024-10-06 14:59:16,172 - INFO - epoch:  87 | train loss: 2.5011 | train accuracy: 98.936 | test loss: 1.8263 | test accuracy: 43.890 | epoch runtime:   4.08 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 14:59:20,301 - INFO - Evaluate Summary Time 1.67s\tLoss 1.8323\t Acc@1 43.2000\t Acc@5 80.4200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:59:20,301 - INFO - Head 78.733\tMid 39.300\tTail 12.867\u001b[0m\n",
      "\u001b[32m2024-10-06 14:59:20,302 - INFO - epoch:  88 | train loss: 2.5032 | train accuracy: 98.984 | test loss: 1.8323 | test accuracy: 43.200 | epoch runtime:   4.13 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 14:59:24,368 - INFO - Evaluate Summary Time 1.67s\tLoss 1.8342\t Acc@1 43.0500\t Acc@5 80.7600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:59:24,368 - INFO - Head 78.100\tMid 39.125\tTail 13.233\u001b[0m\n",
      "\u001b[32m2024-10-06 14:59:24,368 - INFO - epoch:  89 | train loss: 2.5007 | train accuracy: 98.920 | test loss: 1.8342 | test accuracy: 43.050 | epoch runtime:   4.07 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 14:59:28,562 - INFO - Evaluate Summary Time 1.76s\tLoss 1.8464\t Acc@1 42.4600\t Acc@5 79.7800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:59:28,563 - INFO - Head 78.933\tMid 37.875\tTail 12.100\u001b[0m\n",
      "\u001b[32m2024-10-06 14:59:28,563 - INFO - epoch:  90 | train loss: 2.4987 | train accuracy: 99.162 | test loss: 1.8464 | test accuracy: 42.460 | epoch runtime:   4.19 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 14:59:32,616 - INFO - Evaluate Summary Time 1.66s\tLoss 1.8489\t Acc@1 42.6200\t Acc@5 80.4600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:59:32,616 - INFO - Head 78.967\tMid 38.450\tTail 11.833\u001b[0m\n",
      "\u001b[32m2024-10-06 14:59:32,616 - INFO - epoch:  91 | train loss: 2.4960 | train accuracy: 99.275 | test loss: 1.8489 | test accuracy: 42.620 | epoch runtime:   4.05 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 14:59:36,909 - INFO - Evaluate Summary Time 1.78s\tLoss 1.8343\t Acc@1 43.4300\t Acc@5 80.2000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:59:36,909 - INFO - Head 79.267\tMid 40.075\tTail 12.067\u001b[0m\n",
      "\u001b[32m2024-10-06 14:59:36,910 - INFO - epoch:  92 | train loss: 2.4974 | train accuracy: 99.226 | test loss: 1.8343 | test accuracy: 43.430 | epoch runtime:   4.29 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 14:59:41,272 - INFO - Evaluate Summary Time 1.82s\tLoss 1.8296\t Acc@1 43.3700\t Acc@5 80.3800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:59:41,272 - INFO - Head 77.267\tMid 40.450\tTail 13.367\u001b[0m\n",
      "\u001b[32m2024-10-06 14:59:41,273 - INFO - epoch:  93 | train loss: 2.4965 | train accuracy: 99.315 | test loss: 1.8296 | test accuracy: 43.370 | epoch runtime:   4.36 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 14:59:45,436 - INFO - Evaluate Summary Time 1.76s\tLoss 1.8436\t Acc@1 42.6200\t Acc@5 80.2700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:59:45,436 - INFO - Head 80.467\tMid 36.825\tTail 12.500\u001b[0m\n",
      "\u001b[32m2024-10-06 14:59:45,437 - INFO - epoch:  94 | train loss: 2.4953 | train accuracy: 99.242 | test loss: 1.8436 | test accuracy: 42.620 | epoch runtime:   4.16 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 14:59:49,572 - INFO - Evaluate Summary Time 1.72s\tLoss 1.8625\t Acc@1 41.5700\t Acc@5 78.9400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:59:49,572 - INFO - Head 77.833\tMid 36.450\tTail 12.133\u001b[0m\n",
      "\u001b[32m2024-10-06 14:59:49,573 - INFO - epoch:  95 | train loss: 2.4929 | train accuracy: 99.323 | test loss: 1.8625 | test accuracy: 41.570 | epoch runtime:   4.14 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 14:59:53,760 - INFO - Evaluate Summary Time 1.77s\tLoss 1.8403\t Acc@1 42.9400\t Acc@5 81.0000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:59:53,760 - INFO - Head 78.233\tMid 39.050\tTail 12.833\u001b[0m\n",
      "\u001b[32m2024-10-06 14:59:53,760 - INFO - epoch:  96 | train loss: 2.4931 | train accuracy: 99.412 | test loss: 1.8403 | test accuracy: 42.940 | epoch runtime:   4.19 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 14:59:57,812 - INFO - Evaluate Summary Time 1.63s\tLoss 1.8462\t Acc@1 42.9600\t Acc@5 79.9500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 14:59:57,813 - INFO - Head 78.800\tMid 38.875\tTail 12.567\u001b[0m\n",
      "\u001b[32m2024-10-06 14:59:57,813 - INFO - epoch:  97 | train loss: 2.4900 | train accuracy: 99.468 | test loss: 1.8462 | test accuracy: 42.960 | epoch runtime:   4.05 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:00:01,954 - INFO - Evaluate Summary Time 1.75s\tLoss 1.8369\t Acc@1 43.4000\t Acc@5 79.6800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:00:01,955 - INFO - Head 78.433\tMid 39.050\tTail 14.167\u001b[0m\n",
      "\u001b[32m2024-10-06 15:00:01,955 - INFO - epoch:  98 | train loss: 2.4906 | train accuracy: 99.508 | test loss: 1.8369 | test accuracy: 43.400 | epoch runtime:   4.14 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:00:06,132 - INFO - Evaluate Summary Time 1.67s\tLoss 1.8364\t Acc@1 43.3900\t Acc@5 80.2700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:00:06,132 - INFO - Head 78.167\tMid 39.550\tTail 13.733\u001b[0m\n",
      "\u001b[32m2024-10-06 15:00:06,132 - INFO - epoch:  99 | train loss: 2.4917 | train accuracy: 99.629 | test loss: 1.8364 | test accuracy: 43.390 | epoch runtime:   4.18 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:00:10,192 - INFO - Evaluate Summary Time 1.64s\tLoss 1.8541\t Acc@1 42.0600\t Acc@5 79.5900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:00:10,193 - INFO - Head 79.567\tMid 35.275\tTail 13.600\u001b[0m\n",
      "\u001b[32m2024-10-06 15:00:10,193 - INFO - epoch: 100 | train loss: 2.4900 | train accuracy: 99.516 | test loss: 1.8541 | test accuracy: 42.060 | epoch runtime:   4.06 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:00:14,265 - INFO - Evaluate Summary Time 1.69s\tLoss 1.8443\t Acc@1 42.6900\t Acc@5 79.4700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:00:14,265 - INFO - Head 79.167\tMid 38.125\tTail 12.300\u001b[0m\n",
      "\u001b[32m2024-10-06 15:00:14,266 - INFO - epoch: 101 | train loss: 2.4894 | train accuracy: 99.597 | test loss: 1.8443 | test accuracy: 42.690 | epoch runtime:   4.07 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:00:18,380 - INFO - Evaluate Summary Time 1.69s\tLoss 1.8398\t Acc@1 42.9700\t Acc@5 79.7600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:00:18,381 - INFO - Head 78.567\tMid 38.325\tTail 13.567\u001b[0m\n",
      "\u001b[32m2024-10-06 15:00:18,381 - INFO - epoch: 102 | train loss: 2.4888 | train accuracy: 99.645 | test loss: 1.8398 | test accuracy: 42.970 | epoch runtime:   4.12 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:00:22,538 - INFO - Evaluate Summary Time 1.72s\tLoss 1.8461\t Acc@1 42.6100\t Acc@5 79.9200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:00:22,538 - INFO - Head 78.200\tMid 38.950\tTail 11.900\u001b[0m\n",
      "\u001b[32m2024-10-06 15:00:22,539 - INFO - epoch: 103 | train loss: 2.4873 | train accuracy: 99.637 | test loss: 1.8461 | test accuracy: 42.610 | epoch runtime:   4.16 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:00:26,867 - INFO - Evaluate Summary Time 1.88s\tLoss 1.8554\t Acc@1 42.1200\t Acc@5 79.8500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:00:26,867 - INFO - Head 78.267\tMid 38.650\tTail 10.600\u001b[0m\n",
      "\u001b[32m2024-10-06 15:00:26,868 - INFO - epoch: 104 | train loss: 2.4868 | train accuracy: 99.653 | test loss: 1.8554 | test accuracy: 42.120 | epoch runtime:   4.33 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:00:31,113 - INFO - Evaluate Summary Time 1.79s\tLoss 1.8449\t Acc@1 42.8600\t Acc@5 79.8200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:00:31,113 - INFO - Head 78.533\tMid 39.250\tTail 12.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:00:31,114 - INFO - epoch: 105 | train loss: 2.4869 | train accuracy: 99.661 | test loss: 1.8449 | test accuracy: 42.860 | epoch runtime:   4.25 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:00:35,320 - INFO - Evaluate Summary Time 1.75s\tLoss 1.8393\t Acc@1 42.8900\t Acc@5 80.1700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:00:35,320 - INFO - Head 78.100\tMid 39.000\tTail 12.867\u001b[0m\n",
      "\u001b[32m2024-10-06 15:00:35,320 - INFO - epoch: 106 | train loss: 2.4861 | train accuracy: 99.686 | test loss: 1.8393 | test accuracy: 42.890 | epoch runtime:   4.21 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:00:39,526 - INFO - Evaluate Summary Time 1.70s\tLoss 1.8390\t Acc@1 43.3300\t Acc@5 80.2800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:00:39,526 - INFO - Head 79.167\tMid 38.150\tTail 14.400\u001b[0m\n",
      "\u001b[32m2024-10-06 15:00:39,526 - INFO - epoch: 107 | train loss: 2.4853 | train accuracy: 99.661 | test loss: 1.8390 | test accuracy: 43.330 | epoch runtime:   4.21 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:00:43,856 - INFO - Evaluate Summary Time 1.78s\tLoss 1.8345\t Acc@1 43.2800\t Acc@5 79.5100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:00:43,856 - INFO - Head 77.667\tMid 39.950\tTail 13.333\u001b[0m\n",
      "\u001b[32m2024-10-06 15:00:43,856 - INFO - epoch: 108 | train loss: 2.4838 | train accuracy: 99.710 | test loss: 1.8345 | test accuracy: 43.280 | epoch runtime:   4.33 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:00:48,037 - INFO - Evaluate Summary Time 1.74s\tLoss 1.8557\t Acc@1 42.4500\t Acc@5 79.7000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:00:48,037 - INFO - Head 77.667\tMid 38.425\tTail 12.600\u001b[0m\n",
      "\u001b[32m2024-10-06 15:00:48,037 - INFO - epoch: 109 | train loss: 2.4829 | train accuracy: 99.702 | test loss: 1.8557 | test accuracy: 42.450 | epoch runtime:   4.18 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:00:52,177 - INFO - Evaluate Summary Time 1.71s\tLoss 1.8357\t Acc@1 43.2500\t Acc@5 80.0600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:00:52,178 - INFO - Head 78.067\tMid 39.850\tTail 12.967\u001b[0m\n",
      "\u001b[32m2024-10-06 15:00:52,178 - INFO - epoch: 110 | train loss: 2.4834 | train accuracy: 99.766 | test loss: 1.8357 | test accuracy: 43.250 | epoch runtime:   4.14 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:00:56,426 - INFO - Evaluate Summary Time 1.78s\tLoss 1.8361\t Acc@1 43.3500\t Acc@5 79.8300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:00:56,426 - INFO - Head 78.233\tMid 40.225\tTail 12.633\u001b[0m\n",
      "\u001b[32m2024-10-06 15:00:56,426 - INFO - epoch: 111 | train loss: 2.4828 | train accuracy: 99.750 | test loss: 1.8361 | test accuracy: 43.350 | epoch runtime:   4.25 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:01:00,540 - INFO - Evaluate Summary Time 1.65s\tLoss 1.8441\t Acc@1 43.0100\t Acc@5 79.7200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:01:00,540 - INFO - Head 78.833\tMid 38.800\tTail 12.800\u001b[0m\n",
      "\u001b[32m2024-10-06 15:01:00,541 - INFO - epoch: 112 | train loss: 2.4834 | train accuracy: 99.758 | test loss: 1.8441 | test accuracy: 43.010 | epoch runtime:   4.11 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:01:04,700 - INFO - Evaluate Summary Time 1.75s\tLoss 1.8503\t Acc@1 42.0900\t Acc@5 79.4900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:01:04,700 - INFO - Head 78.933\tMid 38.000\tTail 10.700\u001b[0m\n",
      "\u001b[32m2024-10-06 15:01:04,700 - INFO - epoch: 113 | train loss: 2.4827 | train accuracy: 99.742 | test loss: 1.8503 | test accuracy: 42.090 | epoch runtime:   4.16 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:01:08,984 - INFO - Evaluate Summary Time 1.70s\tLoss 1.8332\t Acc@1 43.4200\t Acc@5 80.3400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:01:08,984 - INFO - Head 77.100\tMid 40.850\tTail 13.167\u001b[0m\n",
      "\u001b[32m2024-10-06 15:01:08,984 - INFO - epoch: 114 | train loss: 2.4827 | train accuracy: 99.807 | test loss: 1.8332 | test accuracy: 43.420 | epoch runtime:   4.28 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:01:13,447 - INFO - Evaluate Summary Time 1.78s\tLoss 1.8486\t Acc@1 42.5100\t Acc@5 79.4600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:01:13,448 - INFO - Head 78.000\tMid 38.275\tTail 12.667\u001b[0m\n",
      "\u001b[32m2024-10-06 15:01:13,448 - INFO - epoch: 115 | train loss: 2.4807 | train accuracy: 99.807 | test loss: 1.8486 | test accuracy: 42.510 | epoch runtime:   4.46 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:01:17,613 - INFO - Evaluate Summary Time 1.78s\tLoss 1.8352\t Acc@1 43.2700\t Acc@5 80.0500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:01:17,613 - INFO - Head 77.833\tMid 40.500\tTail 12.400\u001b[0m\n",
      "\u001b[32m2024-10-06 15:01:17,614 - INFO - epoch: 116 | train loss: 2.4803 | train accuracy: 99.782 | test loss: 1.8352 | test accuracy: 43.270 | epoch runtime:   4.17 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:01:21,717 - INFO - Evaluate Summary Time 1.68s\tLoss 1.8521\t Acc@1 42.4700\t Acc@5 79.7500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:01:21,718 - INFO - Head 78.600\tMid 37.975\tTail 12.333\u001b[0m\n",
      "\u001b[32m2024-10-06 15:01:21,718 - INFO - epoch: 117 | train loss: 2.4796 | train accuracy: 99.807 | test loss: 1.8521 | test accuracy: 42.470 | epoch runtime:   4.10 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:01:25,901 - INFO - Evaluate Summary Time 1.72s\tLoss 1.8538\t Acc@1 42.1800\t Acc@5 79.6400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:01:25,901 - INFO - Head 77.333\tMid 37.575\tTail 13.167\u001b[0m\n",
      "\u001b[32m2024-10-06 15:01:25,902 - INFO - epoch: 118 | train loss: 2.4792 | train accuracy: 99.823 | test loss: 1.8538 | test accuracy: 42.180 | epoch runtime:   4.18 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:01:29,914 - INFO - Evaluate Summary Time 1.65s\tLoss 1.8435\t Acc@1 42.8600\t Acc@5 79.7000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:01:29,914 - INFO - Head 78.067\tMid 38.250\tTail 13.800\u001b[0m\n",
      "\u001b[32m2024-10-06 15:01:29,915 - INFO - epoch: 119 | train loss: 2.4786 | train accuracy: 99.847 | test loss: 1.8435 | test accuracy: 42.860 | epoch runtime:   4.01 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:01:34,133 - INFO - Evaluate Summary Time 1.79s\tLoss 1.8552\t Acc@1 42.5600\t Acc@5 79.0500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:01:34,134 - INFO - Head 78.667\tMid 37.475\tTail 13.233\u001b[0m\n",
      "\u001b[32m2024-10-06 15:01:34,134 - INFO - epoch: 120 | train loss: 2.4791 | train accuracy: 99.839 | test loss: 1.8552 | test accuracy: 42.560 | epoch runtime:   4.22 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:01:38,395 - INFO - Evaluate Summary Time 1.81s\tLoss 1.8450\t Acc@1 43.0200\t Acc@5 79.3600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:01:38,396 - INFO - Head 80.067\tMid 38.125\tTail 12.500\u001b[0m\n",
      "\u001b[32m2024-10-06 15:01:38,396 - INFO - epoch: 121 | train loss: 2.4782 | train accuracy: 99.831 | test loss: 1.8450 | test accuracy: 43.020 | epoch runtime:   4.26 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:01:42,645 - INFO - Evaluate Summary Time 1.73s\tLoss 1.8490\t Acc@1 42.6600\t Acc@5 79.8400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:01:42,646 - INFO - Head 77.200\tMid 39.425\tTail 12.433\u001b[0m\n",
      "\u001b[32m2024-10-06 15:01:42,646 - INFO - epoch: 122 | train loss: 2.4784 | train accuracy: 99.855 | test loss: 1.8490 | test accuracy: 42.660 | epoch runtime:   4.25 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:01:46,836 - INFO - Evaluate Summary Time 1.78s\tLoss 1.8410\t Acc@1 42.8700\t Acc@5 79.8800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:01:46,837 - INFO - Head 78.733\tMid 38.725\tTail 12.533\u001b[0m\n",
      "\u001b[32m2024-10-06 15:01:46,837 - INFO - epoch: 123 | train loss: 2.4782 | train accuracy: 99.863 | test loss: 1.8410 | test accuracy: 42.870 | epoch runtime:   4.19 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:01:50,974 - INFO - Evaluate Summary Time 1.73s\tLoss 1.8388\t Acc@1 43.2500\t Acc@5 80.1200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:01:50,974 - INFO - Head 78.500\tMid 38.700\tTail 14.067\u001b[0m\n",
      "\u001b[32m2024-10-06 15:01:50,975 - INFO - epoch: 124 | train loss: 2.4781 | train accuracy: 99.855 | test loss: 1.8388 | test accuracy: 43.250 | epoch runtime:   4.14 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:01:55,123 - INFO - Evaluate Summary Time 1.72s\tLoss 1.8395\t Acc@1 43.4800\t Acc@5 80.0600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:01:55,123 - INFO - Head 78.733\tMid 39.050\tTail 14.133\u001b[0m\n",
      "\u001b[32m2024-10-06 15:01:55,123 - INFO - epoch: 125 | train loss: 2.4792 | train accuracy: 99.839 | test loss: 1.8395 | test accuracy: 43.480 | epoch runtime:   4.15 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:01:59,342 - INFO - Evaluate Summary Time 1.78s\tLoss 1.8364\t Acc@1 43.3400\t Acc@5 79.7400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:01:59,342 - INFO - Head 78.800\tMid 39.825\tTail 12.567\u001b[0m\n",
      "\u001b[32m2024-10-06 15:01:59,343 - INFO - epoch: 126 | train loss: 2.4748 | train accuracy: 99.847 | test loss: 1.8364 | test accuracy: 43.340 | epoch runtime:   4.22 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:02:03,433 - INFO - Evaluate Summary Time 1.71s\tLoss 1.8457\t Acc@1 43.0100\t Acc@5 79.8300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:02:03,434 - INFO - Head 78.867\tMid 38.025\tTail 13.800\u001b[0m\n",
      "\u001b[32m2024-10-06 15:02:03,434 - INFO - epoch: 127 | train loss: 2.4757 | train accuracy: 99.887 | test loss: 1.8457 | test accuracy: 43.010 | epoch runtime:   4.09 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:02:07,523 - INFO - Evaluate Summary Time 1.68s\tLoss 1.8491\t Acc@1 42.4800\t Acc@5 79.2900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:02:07,524 - INFO - Head 77.833\tMid 38.875\tTail 11.933\u001b[0m\n",
      "\u001b[32m2024-10-06 15:02:07,524 - INFO - epoch: 128 | train loss: 2.4755 | train accuracy: 99.831 | test loss: 1.8491 | test accuracy: 42.480 | epoch runtime:   4.09 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:02:11,680 - INFO - Evaluate Summary Time 1.69s\tLoss 1.8511\t Acc@1 42.5100\t Acc@5 79.1700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:02:11,680 - INFO - Head 79.067\tMid 38.000\tTail 11.967\u001b[0m\n",
      "\u001b[32m2024-10-06 15:02:11,681 - INFO - epoch: 129 | train loss: 2.4737 | train accuracy: 99.879 | test loss: 1.8511 | test accuracy: 42.510 | epoch runtime:   4.16 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:02:15,854 - INFO - Evaluate Summary Time 1.75s\tLoss 1.8495\t Acc@1 42.8200\t Acc@5 79.2300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:02:15,854 - INFO - Head 78.733\tMid 38.875\tTail 12.167\u001b[0m\n",
      "\u001b[32m2024-10-06 15:02:15,854 - INFO - epoch: 130 | train loss: 2.4763 | train accuracy: 99.927 | test loss: 1.8495 | test accuracy: 42.820 | epoch runtime:   4.17 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:02:20,050 - INFO - Evaluate Summary Time 1.77s\tLoss 1.8586\t Acc@1 42.4700\t Acc@5 79.2500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:02:20,051 - INFO - Head 78.633\tMid 37.425\tTail 13.033\u001b[0m\n",
      "\u001b[32m2024-10-06 15:02:20,051 - INFO - epoch: 131 | train loss: 2.4748 | train accuracy: 99.871 | test loss: 1.8586 | test accuracy: 42.470 | epoch runtime:   4.20 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:02:24,155 - INFO - Evaluate Summary Time 1.66s\tLoss 1.8431\t Acc@1 43.0400\t Acc@5 79.6200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:02:24,156 - INFO - Head 78.467\tMid 39.050\tTail 12.933\u001b[0m\n",
      "\u001b[32m2024-10-06 15:02:24,156 - INFO - epoch: 132 | train loss: 2.4741 | train accuracy: 99.911 | test loss: 1.8431 | test accuracy: 43.040 | epoch runtime:   4.10 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:02:28,284 - INFO - Evaluate Summary Time 1.72s\tLoss 1.8369\t Acc@1 43.2400\t Acc@5 80.1800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:02:28,284 - INFO - Head 78.500\tMid 38.900\tTail 13.767\u001b[0m\n",
      "\u001b[32m2024-10-06 15:02:28,285 - INFO - epoch: 133 | train loss: 2.4763 | train accuracy: 99.911 | test loss: 1.8369 | test accuracy: 43.240 | epoch runtime:   4.13 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:02:32,396 - INFO - Evaluate Summary Time 1.71s\tLoss 1.8367\t Acc@1 43.2200\t Acc@5 80.1700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:02:32,396 - INFO - Head 77.900\tMid 39.675\tTail 13.267\u001b[0m\n",
      "\u001b[32m2024-10-06 15:02:32,397 - INFO - epoch: 134 | train loss: 2.4747 | train accuracy: 99.871 | test loss: 1.8367 | test accuracy: 43.220 | epoch runtime:   4.11 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:02:36,710 - INFO - Evaluate Summary Time 1.86s\tLoss 1.8452\t Acc@1 43.0800\t Acc@5 79.2200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:02:36,710 - INFO - Head 78.733\tMid 39.875\tTail 11.700\u001b[0m\n",
      "\u001b[32m2024-10-06 15:02:36,711 - INFO - epoch: 135 | train loss: 2.4741 | train accuracy: 99.927 | test loss: 1.8452 | test accuracy: 43.080 | epoch runtime:   4.31 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:02:41,026 - INFO - Evaluate Summary Time 1.82s\tLoss 1.8408\t Acc@1 42.8100\t Acc@5 80.0900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:02:41,027 - INFO - Head 78.867\tMid 38.525\tTail 12.467\u001b[0m\n",
      "\u001b[32m2024-10-06 15:02:41,027 - INFO - epoch: 136 | train loss: 2.4747 | train accuracy: 99.855 | test loss: 1.8408 | test accuracy: 42.810 | epoch runtime:   4.32 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:02:45,198 - INFO - Evaluate Summary Time 1.72s\tLoss 1.8530\t Acc@1 42.4200\t Acc@5 79.4400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:02:45,198 - INFO - Head 79.133\tMid 38.825\tTail 10.500\u001b[0m\n",
      "\u001b[32m2024-10-06 15:02:45,198 - INFO - epoch: 137 | train loss: 2.4737 | train accuracy: 99.903 | test loss: 1.8530 | test accuracy: 42.420 | epoch runtime:   4.17 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:02:49,297 - INFO - Evaluate Summary Time 1.72s\tLoss 1.8489\t Acc@1 42.6100\t Acc@5 79.5400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:02:49,298 - INFO - Head 78.033\tMid 38.275\tTail 12.967\u001b[0m\n",
      "\u001b[32m2024-10-06 15:02:49,298 - INFO - epoch: 138 | train loss: 2.4741 | train accuracy: 99.879 | test loss: 1.8489 | test accuracy: 42.610 | epoch runtime:   4.10 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:02:53,564 - INFO - Evaluate Summary Time 1.85s\tLoss 1.8486\t Acc@1 42.4700\t Acc@5 79.6200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:02:53,564 - INFO - Head 78.867\tMid 38.550\tTail 11.300\u001b[0m\n",
      "\u001b[32m2024-10-06 15:02:53,565 - INFO - epoch: 139 | train loss: 2.4738 | train accuracy: 99.911 | test loss: 1.8486 | test accuracy: 42.470 | epoch runtime:   4.27 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:02:57,616 - INFO - Evaluate Summary Time 1.61s\tLoss 1.8481\t Acc@1 42.7500\t Acc@5 79.3800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:02:57,617 - INFO - Head 78.333\tMid 38.500\tTail 12.833\u001b[0m\n",
      "\u001b[32m2024-10-06 15:02:57,617 - INFO - epoch: 140 | train loss: 2.4749 | train accuracy: 99.927 | test loss: 1.8481 | test accuracy: 42.750 | epoch runtime:   4.05 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:03:01,884 - INFO - Evaluate Summary Time 1.85s\tLoss 1.8352\t Acc@1 43.3400\t Acc@5 79.9100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:03:01,884 - INFO - Head 78.333\tMid 39.925\tTail 12.900\u001b[0m\n",
      "\u001b[32m2024-10-06 15:03:01,884 - INFO - epoch: 141 | train loss: 2.4739 | train accuracy: 99.919 | test loss: 1.8352 | test accuracy: 43.340 | epoch runtime:   4.27 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:03:05,950 - INFO - Evaluate Summary Time 1.63s\tLoss 1.8461\t Acc@1 42.6500\t Acc@5 79.6300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:03:05,950 - INFO - Head 77.700\tMid 39.325\tTail 12.033\u001b[0m\n",
      "\u001b[32m2024-10-06 15:03:05,951 - INFO - epoch: 142 | train loss: 2.4742 | train accuracy: 99.903 | test loss: 1.8461 | test accuracy: 42.650 | epoch runtime:   4.07 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:03:10,066 - INFO - Evaluate Summary Time 1.71s\tLoss 1.8432\t Acc@1 42.8900\t Acc@5 79.6200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:03:10,066 - INFO - Head 77.933\tMid 39.050\tTail 12.967\u001b[0m\n",
      "\u001b[32m2024-10-06 15:03:10,066 - INFO - epoch: 143 | train loss: 2.4763 | train accuracy: 99.927 | test loss: 1.8432 | test accuracy: 42.890 | epoch runtime:   4.12 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:03:14,202 - INFO - Evaluate Summary Time 1.74s\tLoss 1.8451\t Acc@1 43.0200\t Acc@5 80.0000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:03:14,202 - INFO - Head 79.100\tMid 38.675\tTail 12.733\u001b[0m\n",
      "\u001b[32m2024-10-06 15:03:14,203 - INFO - epoch: 144 | train loss: 2.4741 | train accuracy: 99.927 | test loss: 1.8451 | test accuracy: 43.020 | epoch runtime:   4.14 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:03:18,278 - INFO - Evaluate Summary Time 1.67s\tLoss 1.8445\t Acc@1 42.7900\t Acc@5 79.7900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:03:18,278 - INFO - Head 77.167\tMid 39.350\tTail 13.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:03:18,278 - INFO - epoch: 145 | train loss: 2.4732 | train accuracy: 99.903 | test loss: 1.8445 | test accuracy: 42.790 | epoch runtime:   4.08 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:03:22,395 - INFO - Evaluate Summary Time 1.72s\tLoss 1.8343\t Acc@1 43.3300\t Acc@5 80.0600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:03:22,395 - INFO - Head 78.067\tMid 39.950\tTail 13.100\u001b[0m\n",
      "\u001b[32m2024-10-06 15:03:22,395 - INFO - epoch: 146 | train loss: 2.4735 | train accuracy: 99.927 | test loss: 1.8343 | test accuracy: 43.330 | epoch runtime:   4.12 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:03:26,504 - INFO - Evaluate Summary Time 1.69s\tLoss 1.8456\t Acc@1 42.9300\t Acc@5 79.6400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:03:26,504 - INFO - Head 78.467\tMid 38.650\tTail 13.100\u001b[0m\n",
      "\u001b[32m2024-10-06 15:03:26,504 - INFO - epoch: 147 | train loss: 2.4729 | train accuracy: 99.952 | test loss: 1.8456 | test accuracy: 42.930 | epoch runtime:   4.11 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:03:30,554 - INFO - Evaluate Summary Time 1.69s\tLoss 1.8465\t Acc@1 42.7500\t Acc@5 79.4100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:03:30,554 - INFO - Head 78.833\tMid 38.400\tTail 12.467\u001b[0m\n",
      "\u001b[32m2024-10-06 15:03:30,555 - INFO - epoch: 148 | train loss: 2.4738 | train accuracy: 99.895 | test loss: 1.8465 | test accuracy: 42.750 | epoch runtime:   4.05 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:03:34,723 - INFO - Evaluate Summary Time 1.78s\tLoss 1.8445\t Acc@1 42.6500\t Acc@5 79.4600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:03:34,723 - INFO - Head 79.133\tMid 38.400\tTail 11.833\u001b[0m\n",
      "\u001b[32m2024-10-06 15:03:34,724 - INFO - epoch: 149 | train loss: 2.4740 | train accuracy: 99.895 | test loss: 1.8445 | test accuracy: 42.650 | epoch runtime:   4.17 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "\u001b[32m2024-10-06 15:03:39,009 - INFO - Evaluate Summary Time 1.73s\tLoss 1.8437\t Acc@1 42.9600\t Acc@5 79.8400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:03:39,010 - INFO - Head 78.967\tMid 38.250\tTail 13.233\u001b[0m\n",
      "\u001b[32m2024-10-06 15:03:39,010 - INFO - epoch: 150 | train loss: 2.4743 | train accuracy: 99.911 | test loss: 1.8437 | test accuracy: 42.960 | epoch runtime:   4.29 sec | best accuracy: 44.550 @ epoch: 057\u001b[0m\n",
      "Runtime of this script /home/zyx/zhengjinpeng/PNP/cifar.py : 626.7 seconds (0.174 hours)\n",
      "Config:\n",
      "{\n",
      "    database: Datasets\n",
      "    dataset: cifar10\n",
      "    n_classes: 10\n",
      "    rescale_size: 32\n",
      "    crop_size: 32\n",
      "    cfg_file: ./config/cifar10.cfg\n",
      "    synthetic_data: cifar80no\n",
      "    noise_type: asymmetric\n",
      "    closeset_ratio: 0.2\n",
      "    r_ood: 0.2\n",
      "    r_imb: 0.01\n",
      "    gpu: 0\n",
      "    net: cnn\n",
      "    batch_size: 128\n",
      "    lr: 0.001\n",
      "    lr_decay: cosine\n",
      "    weight_decay: 1e-05\n",
      "    opt: adam\n",
      "    warmup_epochs: 5\n",
      "    warmup_lr_scale: 10.0\n",
      "    epochs: 150\n",
      "    save_model: False\n",
      "    use_fp16: False\n",
      "    use_grad_accumulate: False\n",
      "    project: \n",
      "    log: PENIOC\n",
      "    epsilon: 0.5\n",
      "    temperature: 0.1\n",
      "    eta: 0.5\n",
      "    alpha: 0.0\n",
      "    beta: 1.0\n",
      "    gamma: 1.0\n",
      "    omega: 0.1\n",
      "    rho: 1.0\n",
      "    loss_func_aux: mae\n",
      "    weighting: soft\n",
      "    neg_cons: False\n",
      "    activation: tanh\n",
      "    ablation: False\n",
      "    log_freq: 1\n",
      "    asym: True\n",
      "}\n",
      "\n",
      "Available GPUs Index : 0\n",
      "using CIFAR-10...\n",
      "Built imbalanced dataset, r_imb=0.01\n",
      "Mixing in OOD noise, r_ood=0.2\n",
      "Mixing in ID asym noise, r_id=0.2\n",
      "using CIFAR-10...\n",
      "\u001b[32m2024-10-06 15:03:45,736 - INFO - Categories: 10, Training Samples: 12406, Testing Samples: 10000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:03:45,736 - INFO - Optimizer: adam\u001b[0m\n",
      "\u001b[32m2024-10-06 15:03:45,736 - INFO - Accumulate gradients every 1 iterations --> Acutal batch size is 128\u001b[0m\n",
      "\u001b[32m2024-10-06 15:03:50,343 - INFO - Evaluate Summary Time 1.69s\tLoss 2.2271\t Acc@1 20.2900\t Acc@5 59.4200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:03:50,343 - INFO - Head 66.433\tMid 0.900\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:03:50,344 - INFO - epoch:   1 | train loss: 2.2191 | train accuracy: 46.389 | test loss: 2.2271 | test accuracy: 20.290 | epoch runtime:   4.61 sec | best accuracy: 20.290 @ epoch: 001\u001b[0m\n",
      "\u001b[32m2024-10-06 15:03:54,134 - INFO - Evaluate Summary Time 1.71s\tLoss 2.1997\t Acc@1 21.3700\t Acc@5 65.5900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:03:54,134 - INFO - Head 64.967\tMid 4.700\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:03:54,134 - INFO - epoch:   2 | train loss: 2.1089 | train accuracy: 54.143 | test loss: 2.1997 | test accuracy: 21.370 | epoch runtime:   3.79 sec | best accuracy: 21.370 @ epoch: 002\u001b[0m\n",
      "\u001b[32m2024-10-06 15:03:57,923 - INFO - Evaluate Summary Time 1.69s\tLoss 2.1451\t Acc@1 22.7000\t Acc@5 68.6400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:03:57,923 - INFO - Head 65.467\tMid 7.600\tTail 0.067\u001b[0m\n",
      "\u001b[32m2024-10-06 15:03:57,924 - INFO - epoch:   3 | train loss: 2.0953 | train accuracy: 56.134 | test loss: 2.1451 | test accuracy: 22.700 | epoch runtime:   3.79 sec | best accuracy: 22.700 @ epoch: 003\u001b[0m\n",
      "\u001b[32m2024-10-06 15:04:01,693 - INFO - Evaluate Summary Time 1.72s\tLoss 2.1231\t Acc@1 23.7000\t Acc@5 66.2900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:04:01,693 - INFO - Head 69.833\tMid 6.875\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:04:01,694 - INFO - epoch:   4 | train loss: 2.0766 | train accuracy: 58.609 | test loss: 2.1231 | test accuracy: 23.700 | epoch runtime:   3.77 sec | best accuracy: 23.700 @ epoch: 004\u001b[0m\n",
      "\u001b[32m2024-10-06 15:04:05,573 - INFO - Evaluate Summary Time 1.76s\tLoss 2.0701\t Acc@1 27.5600\t Acc@5 66.1600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:04:05,573 - INFO - Head 74.533\tMid 13.000\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:04:05,574 - INFO - epoch:   5 | train loss: 2.0671 | train accuracy: 60.108 | test loss: 2.0701 | test accuracy: 27.560 | epoch runtime:   3.88 sec | best accuracy: 27.560 @ epoch: 005\u001b[0m\n",
      "\u001b[32m2024-10-06 15:04:09,734 - INFO - Evaluate Summary Time 1.70s\tLoss 2.0661\t Acc@1 28.1500\t Acc@5 74.2900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:04:09,734 - INFO - Head 72.833\tMid 15.750\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:04:09,735 - INFO - epoch:   6 | train loss: 2.6538 | train accuracy: 62.333 | test loss: 2.0661 | test accuracy: 28.150 | epoch runtime:   4.16 sec | best accuracy: 28.150 @ epoch: 006\u001b[0m\n",
      "\u001b[32m2024-10-06 15:04:13,946 - INFO - Evaluate Summary Time 1.80s\tLoss 2.0518\t Acc@1 28.9100\t Acc@5 75.0000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:04:13,947 - INFO - Head 75.600\tMid 15.575\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:04:13,947 - INFO - epoch:   7 | train loss: 2.6186 | train accuracy: 62.833 | test loss: 2.0518 | test accuracy: 28.910 | epoch runtime:   4.21 sec | best accuracy: 28.910 @ epoch: 007\u001b[0m\n",
      "\u001b[32m2024-10-06 15:04:18,022 - INFO - Evaluate Summary Time 1.66s\tLoss 2.0439\t Acc@1 29.0300\t Acc@5 75.0300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:04:18,022 - INFO - Head 76.300\tMid 15.350\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:04:18,022 - INFO - epoch:   8 | train loss: 2.6068 | train accuracy: 63.348 | test loss: 2.0439 | test accuracy: 29.030 | epoch runtime:   4.08 sec | best accuracy: 29.030 @ epoch: 008\u001b[0m\n",
      "\u001b[32m2024-10-06 15:04:22,157 - INFO - Evaluate Summary Time 1.73s\tLoss 2.0290\t Acc@1 30.0300\t Acc@5 76.8100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:04:22,158 - INFO - Head 76.833\tMid 17.450\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:04:22,158 - INFO - epoch:   9 | train loss: 2.6010 | train accuracy: 63.550 | test loss: 2.0290 | test accuracy: 30.030 | epoch runtime:   4.14 sec | best accuracy: 30.030 @ epoch: 009\u001b[0m\n",
      "\u001b[32m2024-10-06 15:04:26,376 - INFO - Evaluate Summary Time 1.75s\tLoss 2.0032\t Acc@1 32.1400\t Acc@5 78.7400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:04:26,376 - INFO - Head 79.033\tMid 21.075\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:04:26,377 - INFO - epoch:  10 | train loss: 2.5990 | train accuracy: 63.864 | test loss: 2.0032 | test accuracy: 32.140 | epoch runtime:   4.22 sec | best accuracy: 32.140 @ epoch: 010\u001b[0m\n",
      "\u001b[32m2024-10-06 15:04:30,521 - INFO - Evaluate Summary Time 1.75s\tLoss 2.0097\t Acc@1 31.2600\t Acc@5 78.4400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:04:30,521 - INFO - Head 77.633\tMid 19.875\tTail 0.067\u001b[0m\n",
      "\u001b[32m2024-10-06 15:04:30,522 - INFO - epoch:  11 | train loss: 2.5903 | train accuracy: 64.711 | test loss: 2.0097 | test accuracy: 31.260 | epoch runtime:   4.15 sec | best accuracy: 32.140 @ epoch: 010\u001b[0m\n",
      "\u001b[32m2024-10-06 15:04:34,729 - INFO - Evaluate Summary Time 1.80s\tLoss 2.0105\t Acc@1 30.8900\t Acc@5 78.7500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:04:34,730 - INFO - Head 79.167\tMid 17.750\tTail 0.133\u001b[0m\n",
      "\u001b[32m2024-10-06 15:04:34,730 - INFO - epoch:  12 | train loss: 2.5818 | train accuracy: 64.670 | test loss: 2.0105 | test accuracy: 30.890 | epoch runtime:   4.21 sec | best accuracy: 32.140 @ epoch: 010\u001b[0m\n",
      "\u001b[32m2024-10-06 15:04:39,035 - INFO - Evaluate Summary Time 1.78s\tLoss 1.9861\t Acc@1 32.3300\t Acc@5 79.7800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:04:39,035 - INFO - Head 79.300\tMid 21.175\tTail 0.233\u001b[0m\n",
      "\u001b[32m2024-10-06 15:04:39,035 - INFO - epoch:  13 | train loss: 2.5830 | train accuracy: 64.936 | test loss: 1.9861 | test accuracy: 32.330 | epoch runtime:   4.31 sec | best accuracy: 32.330 @ epoch: 013\u001b[0m\n",
      "\u001b[32m2024-10-06 15:04:43,208 - INFO - Evaluate Summary Time 1.71s\tLoss 1.9673\t Acc@1 34.5100\t Acc@5 80.1400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:04:43,208 - INFO - Head 79.733\tMid 26.275\tTail 0.267\u001b[0m\n",
      "\u001b[32m2024-10-06 15:04:43,208 - INFO - epoch:  14 | train loss: 2.5799 | train accuracy: 65.597 | test loss: 1.9673 | test accuracy: 34.510 | epoch runtime:   4.17 sec | best accuracy: 34.510 @ epoch: 014\u001b[0m\n",
      "\u001b[32m2024-10-06 15:04:47,429 - INFO - Evaluate Summary Time 1.73s\tLoss 1.9671\t Acc@1 33.4800\t Acc@5 81.3800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:04:47,430 - INFO - Head 79.933\tMid 23.600\tTail 0.200\u001b[0m\n",
      "\u001b[32m2024-10-06 15:04:47,430 - INFO - epoch:  15 | train loss: 2.5771 | train accuracy: 66.307 | test loss: 1.9671 | test accuracy: 33.480 | epoch runtime:   4.22 sec | best accuracy: 34.510 @ epoch: 014\u001b[0m\n",
      "\u001b[32m2024-10-06 15:04:51,573 - INFO - Evaluate Summary Time 1.74s\tLoss 1.9617\t Acc@1 33.5500\t Acc@5 82.2400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:04:51,574 - INFO - Head 79.567\tMid 24.150\tTail 0.067\u001b[0m\n",
      "\u001b[32m2024-10-06 15:04:51,574 - INFO - epoch:  16 | train loss: 2.5795 | train accuracy: 66.677 | test loss: 1.9617 | test accuracy: 33.550 | epoch runtime:   4.14 sec | best accuracy: 34.510 @ epoch: 014\u001b[0m\n",
      "\u001b[32m2024-10-06 15:04:55,709 - INFO - Evaluate Summary Time 1.73s\tLoss 1.9383\t Acc@1 34.7900\t Acc@5 82.9700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:04:55,709 - INFO - Head 81.433\tMid 25.475\tTail 0.567\u001b[0m\n",
      "\u001b[32m2024-10-06 15:04:55,710 - INFO - epoch:  17 | train loss: 2.5776 | train accuracy: 67.048 | test loss: 1.9383 | test accuracy: 34.790 | epoch runtime:   4.14 sec | best accuracy: 34.790 @ epoch: 017\u001b[0m\n",
      "\u001b[32m2024-10-06 15:04:59,801 - INFO - Evaluate Summary Time 1.69s\tLoss 1.9267\t Acc@1 36.6600\t Acc@5 81.8600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:04:59,802 - INFO - Head 80.467\tMid 30.275\tTail 1.367\u001b[0m\n",
      "\u001b[32m2024-10-06 15:04:59,802 - INFO - epoch:  18 | train loss: 2.5765 | train accuracy: 67.709 | test loss: 1.9267 | test accuracy: 36.660 | epoch runtime:   4.09 sec | best accuracy: 36.660 @ epoch: 018\u001b[0m\n",
      "\u001b[32m2024-10-06 15:05:03,930 - INFO - Evaluate Summary Time 1.71s\tLoss 1.9242\t Acc@1 36.6100\t Acc@5 84.9300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:05:03,931 - INFO - Head 80.233\tMid 30.625\tTail 0.967\u001b[0m\n",
      "\u001b[32m2024-10-06 15:05:03,931 - INFO - epoch:  19 | train loss: 2.5768 | train accuracy: 68.241 | test loss: 1.9242 | test accuracy: 36.610 | epoch runtime:   4.13 sec | best accuracy: 36.660 @ epoch: 018\u001b[0m\n",
      "\u001b[32m2024-10-06 15:05:08,018 - INFO - Evaluate Summary Time 1.71s\tLoss 1.9027\t Acc@1 36.4300\t Acc@5 82.6500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:05:08,019 - INFO - Head 85.400\tMid 26.875\tTail 0.200\u001b[0m\n",
      "\u001b[32m2024-10-06 15:05:08,019 - INFO - epoch:  20 | train loss: 2.5697 | train accuracy: 68.685 | test loss: 1.9027 | test accuracy: 36.430 | epoch runtime:   4.09 sec | best accuracy: 36.660 @ epoch: 018\u001b[0m\n",
      "\u001b[32m2024-10-06 15:05:12,152 - INFO - Evaluate Summary Time 1.67s\tLoss 1.8483\t Acc@1 40.7200\t Acc@5 85.1800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:05:12,153 - INFO - Head 84.833\tMid 37.200\tTail 1.300\u001b[0m\n",
      "\u001b[32m2024-10-06 15:05:12,153 - INFO - epoch:  21 | train loss: 2.5719 | train accuracy: 69.120 | test loss: 1.8483 | test accuracy: 40.720 | epoch runtime:   4.13 sec | best accuracy: 40.720 @ epoch: 021\u001b[0m\n",
      "\u001b[32m2024-10-06 15:05:16,297 - INFO - Evaluate Summary Time 1.72s\tLoss 1.8912\t Acc@1 35.9600\t Acc@5 86.4100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:05:16,298 - INFO - Head 84.333\tMid 25.250\tTail 1.867\u001b[0m\n",
      "\u001b[32m2024-10-06 15:05:16,298 - INFO - epoch:  22 | train loss: 2.5685 | train accuracy: 69.974 | test loss: 1.8912 | test accuracy: 35.960 | epoch runtime:   4.14 sec | best accuracy: 40.720 @ epoch: 021\u001b[0m\n",
      "\u001b[32m2024-10-06 15:05:20,351 - INFO - Evaluate Summary Time 1.65s\tLoss 1.9144\t Acc@1 35.1300\t Acc@5 86.0300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:05:20,351 - INFO - Head 82.467\tMid 24.625\tTail 1.800\u001b[0m\n",
      "\u001b[32m2024-10-06 15:05:20,351 - INFO - epoch:  23 | train loss: 2.5648 | train accuracy: 70.571 | test loss: 1.9144 | test accuracy: 35.130 | epoch runtime:   4.05 sec | best accuracy: 40.720 @ epoch: 021\u001b[0m\n",
      "\u001b[32m2024-10-06 15:05:24,554 - INFO - Evaluate Summary Time 1.78s\tLoss 1.8306\t Acc@1 40.2100\t Acc@5 85.8100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:05:24,555 - INFO - Head 84.233\tMid 35.750\tTail 2.133\u001b[0m\n",
      "\u001b[32m2024-10-06 15:05:24,555 - INFO - epoch:  24 | train loss: 2.5620 | train accuracy: 71.353 | test loss: 1.8306 | test accuracy: 40.210 | epoch runtime:   4.20 sec | best accuracy: 40.720 @ epoch: 021\u001b[0m\n",
      "\u001b[32m2024-10-06 15:05:28,695 - INFO - Evaluate Summary Time 1.71s\tLoss 1.8593\t Acc@1 39.4300\t Acc@5 85.6500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:05:28,695 - INFO - Head 83.833\tMid 33.700\tTail 2.667\u001b[0m\n",
      "\u001b[32m2024-10-06 15:05:28,695 - INFO - epoch:  25 | train loss: 2.5594 | train accuracy: 71.925 | test loss: 1.8593 | test accuracy: 39.430 | epoch runtime:   4.14 sec | best accuracy: 40.720 @ epoch: 021\u001b[0m\n",
      "\u001b[32m2024-10-06 15:05:32,800 - INFO - Evaluate Summary Time 1.71s\tLoss 1.8584\t Acc@1 39.4600\t Acc@5 87.1900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:05:32,800 - INFO - Head 79.467\tMid 35.650\tTail 4.533\u001b[0m\n",
      "\u001b[32m2024-10-06 15:05:32,801 - INFO - epoch:  26 | train loss: 2.5584 | train accuracy: 72.602 | test loss: 1.8584 | test accuracy: 39.460 | epoch runtime:   4.11 sec | best accuracy: 40.720 @ epoch: 021\u001b[0m\n",
      "\u001b[32m2024-10-06 15:05:37,037 - INFO - Evaluate Summary Time 1.71s\tLoss 1.8714\t Acc@1 39.4200\t Acc@5 86.6800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:05:37,037 - INFO - Head 84.233\tMid 33.125\tTail 3.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:05:37,037 - INFO - epoch:  27 | train loss: 2.5541 | train accuracy: 72.981 | test loss: 1.8714 | test accuracy: 39.420 | epoch runtime:   4.24 sec | best accuracy: 40.720 @ epoch: 021\u001b[0m\n",
      "\u001b[32m2024-10-06 15:05:41,308 - INFO - Evaluate Summary Time 1.81s\tLoss 1.7790\t Acc@1 44.3000\t Acc@5 86.4100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:05:41,308 - INFO - Head 85.467\tMid 40.850\tTail 7.733\u001b[0m\n",
      "\u001b[32m2024-10-06 15:05:41,309 - INFO - epoch:  28 | train loss: 2.5529 | train accuracy: 73.569 | test loss: 1.7790 | test accuracy: 44.300 | epoch runtime:   4.27 sec | best accuracy: 44.300 @ epoch: 028\u001b[0m\n",
      "\u001b[32m2024-10-06 15:05:45,512 - INFO - Evaluate Summary Time 1.70s\tLoss 1.7957\t Acc@1 40.9400\t Acc@5 88.2300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:05:45,512 - INFO - Head 84.367\tMid 37.050\tTail 2.700\u001b[0m\n",
      "\u001b[32m2024-10-06 15:05:45,512 - INFO - epoch:  29 | train loss: 2.5479 | train accuracy: 74.537 | test loss: 1.7957 | test accuracy: 40.940 | epoch runtime:   4.20 sec | best accuracy: 44.300 @ epoch: 028\u001b[0m\n",
      "\u001b[32m2024-10-06 15:05:49,634 - INFO - Evaluate Summary Time 1.74s\tLoss 1.8384\t Acc@1 40.4200\t Acc@5 84.6500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:05:49,635 - INFO - Head 85.800\tMid 34.400\tTail 3.067\u001b[0m\n",
      "\u001b[32m2024-10-06 15:05:49,635 - INFO - epoch:  30 | train loss: 2.5487 | train accuracy: 75.601 | test loss: 1.8384 | test accuracy: 40.420 | epoch runtime:   4.12 sec | best accuracy: 44.300 @ epoch: 028\u001b[0m\n",
      "\u001b[32m2024-10-06 15:05:53,802 - INFO - Evaluate Summary Time 1.71s\tLoss 1.8076\t Acc@1 42.1700\t Acc@5 86.9000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:05:53,802 - INFO - Head 85.700\tMid 37.225\tTail 5.233\u001b[0m\n",
      "\u001b[32m2024-10-06 15:05:53,802 - INFO - epoch:  31 | train loss: 2.5413 | train accuracy: 75.681 | test loss: 1.8076 | test accuracy: 42.170 | epoch runtime:   4.17 sec | best accuracy: 44.300 @ epoch: 028\u001b[0m\n",
      "\u001b[32m2024-10-06 15:05:57,917 - INFO - Evaluate Summary Time 1.67s\tLoss 1.7684\t Acc@1 41.6900\t Acc@5 88.4200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:05:57,917 - INFO - Head 85.533\tMid 36.025\tTail 5.400\u001b[0m\n",
      "\u001b[32m2024-10-06 15:05:57,918 - INFO - epoch:  32 | train loss: 2.5422 | train accuracy: 76.729 | test loss: 1.7684 | test accuracy: 41.690 | epoch runtime:   4.12 sec | best accuracy: 44.300 @ epoch: 028\u001b[0m\n",
      "\u001b[32m2024-10-06 15:06:02,024 - INFO - Evaluate Summary Time 1.69s\tLoss 1.7234\t Acc@1 45.0700\t Acc@5 89.9400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:06:02,024 - INFO - Head 83.033\tMid 44.875\tTail 7.367\u001b[0m\n",
      "\u001b[32m2024-10-06 15:06:02,024 - INFO - epoch:  33 | train loss: 2.5383 | train accuracy: 77.221 | test loss: 1.7234 | test accuracy: 45.070 | epoch runtime:   4.11 sec | best accuracy: 45.070 @ epoch: 033\u001b[0m\n",
      "\u001b[32m2024-10-06 15:06:06,096 - INFO - Evaluate Summary Time 1.64s\tLoss 1.7855\t Acc@1 43.7000\t Acc@5 88.0400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:06:06,096 - INFO - Head 83.933\tMid 41.800\tTail 6.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:06:06,096 - INFO - epoch:  34 | train loss: 2.5332 | train accuracy: 78.188 | test loss: 1.7855 | test accuracy: 43.700 | epoch runtime:   4.07 sec | best accuracy: 45.070 @ epoch: 033\u001b[0m\n",
      "\u001b[32m2024-10-06 15:06:10,295 - INFO - Evaluate Summary Time 1.79s\tLoss 1.7708\t Acc@1 45.0500\t Acc@5 88.4900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:06:10,295 - INFO - Head 83.933\tMid 44.375\tTail 7.067\u001b[0m\n",
      "\u001b[32m2024-10-06 15:06:10,296 - INFO - epoch:  35 | train loss: 2.5306 | train accuracy: 78.889 | test loss: 1.7708 | test accuracy: 45.050 | epoch runtime:   4.20 sec | best accuracy: 45.070 @ epoch: 033\u001b[0m\n",
      "\u001b[32m2024-10-06 15:06:14,407 - INFO - Evaluate Summary Time 1.70s\tLoss 1.6951\t Acc@1 45.6300\t Acc@5 90.7500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:06:14,407 - INFO - Head 87.033\tMid 45.625\tTail 4.233\u001b[0m\n",
      "\u001b[32m2024-10-06 15:06:14,407 - INFO - epoch:  36 | train loss: 2.5283 | train accuracy: 79.832 | test loss: 1.6951 | test accuracy: 45.630 | epoch runtime:   4.11 sec | best accuracy: 45.630 @ epoch: 036\u001b[0m\n",
      "\u001b[32m2024-10-06 15:06:18,703 - INFO - Evaluate Summary Time 1.83s\tLoss 1.7416\t Acc@1 44.9100\t Acc@5 88.2800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:06:18,704 - INFO - Head 82.633\tMid 42.500\tTail 10.400\u001b[0m\n",
      "\u001b[32m2024-10-06 15:06:18,704 - INFO - epoch:  37 | train loss: 2.5244 | train accuracy: 80.461 | test loss: 1.7416 | test accuracy: 44.910 | epoch runtime:   4.30 sec | best accuracy: 45.630 @ epoch: 036\u001b[0m\n",
      "\u001b[32m2024-10-06 15:06:22,833 - INFO - Evaluate Summary Time 1.69s\tLoss 1.7310\t Acc@1 45.3700\t Acc@5 88.1000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:06:22,834 - INFO - Head 84.100\tMid 43.550\tTail 9.067\u001b[0m\n",
      "\u001b[32m2024-10-06 15:06:22,834 - INFO - epoch:  38 | train loss: 2.5227 | train accuracy: 81.227 | test loss: 1.7310 | test accuracy: 45.370 | epoch runtime:   4.13 sec | best accuracy: 45.630 @ epoch: 036\u001b[0m\n",
      "\u001b[32m2024-10-06 15:06:27,005 - INFO - Evaluate Summary Time 1.68s\tLoss 1.6982\t Acc@1 45.4800\t Acc@5 90.0000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:06:27,006 - INFO - Head 83.467\tMid 44.875\tTail 8.300\u001b[0m\n",
      "\u001b[32m2024-10-06 15:06:27,006 - INFO - epoch:  39 | train loss: 2.5185 | train accuracy: 82.113 | test loss: 1.6982 | test accuracy: 45.480 | epoch runtime:   4.17 sec | best accuracy: 45.630 @ epoch: 036\u001b[0m\n",
      "\u001b[32m2024-10-06 15:06:31,070 - INFO - Evaluate Summary Time 1.66s\tLoss 1.7389\t Acc@1 44.4300\t Acc@5 87.0100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:06:31,070 - INFO - Head 83.433\tMid 44.300\tTail 5.600\u001b[0m\n",
      "\u001b[32m2024-10-06 15:06:31,071 - INFO - epoch:  40 | train loss: 2.5144 | train accuracy: 83.443 | test loss: 1.7389 | test accuracy: 44.430 | epoch runtime:   4.06 sec | best accuracy: 45.630 @ epoch: 036\u001b[0m\n",
      "\u001b[32m2024-10-06 15:06:35,111 - INFO - Evaluate Summary Time 1.69s\tLoss 1.6934\t Acc@1 47.0300\t Acc@5 89.7800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:06:35,111 - INFO - Head 82.800\tMid 47.725\tTail 10.333\u001b[0m\n",
      "\u001b[32m2024-10-06 15:06:35,112 - INFO - epoch:  41 | train loss: 2.5136 | train accuracy: 83.838 | test loss: 1.6934 | test accuracy: 47.030 | epoch runtime:   4.04 sec | best accuracy: 47.030 @ epoch: 041\u001b[0m\n",
      "\u001b[32m2024-10-06 15:06:39,450 - INFO - Evaluate Summary Time 1.78s\tLoss 1.7155\t Acc@1 45.6300\t Acc@5 89.7300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:06:39,450 - INFO - Head 82.167\tMid 45.900\tTail 8.733\u001b[0m\n",
      "\u001b[32m2024-10-06 15:06:39,450 - INFO - epoch:  42 | train loss: 2.5085 | train accuracy: 84.943 | test loss: 1.7155 | test accuracy: 45.630 | epoch runtime:   4.34 sec | best accuracy: 47.030 @ epoch: 041\u001b[0m\n",
      "\u001b[32m2024-10-06 15:06:43,829 - INFO - Evaluate Summary Time 1.83s\tLoss 1.6563\t Acc@1 47.9600\t Acc@5 89.0500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:06:43,830 - INFO - Head 83.200\tMid 47.325\tTail 13.567\u001b[0m\n",
      "\u001b[32m2024-10-06 15:06:43,830 - INFO - epoch:  43 | train loss: 2.5040 | train accuracy: 85.265 | test loss: 1.6563 | test accuracy: 47.960 | epoch runtime:   4.38 sec | best accuracy: 47.960 @ epoch: 043\u001b[0m\n",
      "\u001b[32m2024-10-06 15:06:47,940 - INFO - Evaluate Summary Time 1.68s\tLoss 1.7133\t Acc@1 45.7200\t Acc@5 87.6400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:06:47,940 - INFO - Head 83.567\tMid 45.325\tTail 8.400\u001b[0m\n",
      "\u001b[32m2024-10-06 15:06:47,941 - INFO - epoch:  44 | train loss: 2.5016 | train accuracy: 87.313 | test loss: 1.7133 | test accuracy: 45.720 | epoch runtime:   4.11 sec | best accuracy: 47.960 @ epoch: 043\u001b[0m\n",
      "\u001b[32m2024-10-06 15:06:51,989 - INFO - Evaluate Summary Time 1.66s\tLoss 1.6354\t Acc@1 48.3800\t Acc@5 89.8300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:06:51,990 - INFO - Head 82.733\tMid 49.350\tTail 12.733\u001b[0m\n",
      "\u001b[32m2024-10-06 15:06:51,990 - INFO - epoch:  45 | train loss: 2.4961 | train accuracy: 88.046 | test loss: 1.6354 | test accuracy: 48.380 | epoch runtime:   4.05 sec | best accuracy: 48.380 @ epoch: 045\u001b[0m\n",
      "\u001b[32m2024-10-06 15:06:56,158 - INFO - Evaluate Summary Time 1.74s\tLoss 1.7007\t Acc@1 45.8900\t Acc@5 89.3200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:06:56,158 - INFO - Head 82.833\tMid 43.500\tTail 12.133\u001b[0m\n",
      "\u001b[32m2024-10-06 15:06:56,159 - INFO - epoch:  46 | train loss: 2.4942 | train accuracy: 89.030 | test loss: 1.7007 | test accuracy: 45.890 | epoch runtime:   4.17 sec | best accuracy: 48.380 @ epoch: 045\u001b[0m\n",
      "\u001b[32m2024-10-06 15:07:00,257 - INFO - Evaluate Summary Time 1.70s\tLoss 1.6531\t Acc@1 47.6600\t Acc@5 89.9700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:07:00,258 - INFO - Head 83.300\tMid 48.575\tTail 10.800\u001b[0m\n",
      "\u001b[32m2024-10-06 15:07:00,258 - INFO - epoch:  47 | train loss: 2.4892 | train accuracy: 89.973 | test loss: 1.6531 | test accuracy: 47.660 | epoch runtime:   4.10 sec | best accuracy: 48.380 @ epoch: 045\u001b[0m\n",
      "\u001b[32m2024-10-06 15:07:04,430 - INFO - Evaluate Summary Time 1.75s\tLoss 1.7639\t Acc@1 42.7400\t Acc@5 88.5900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:07:04,430 - INFO - Head 81.267\tMid 39.125\tTail 9.033\u001b[0m\n",
      "\u001b[32m2024-10-06 15:07:04,430 - INFO - epoch:  48 | train loss: 2.4836 | train accuracy: 90.440 | test loss: 1.7639 | test accuracy: 42.740 | epoch runtime:   4.17 sec | best accuracy: 48.380 @ epoch: 045\u001b[0m\n",
      "\u001b[32m2024-10-06 15:07:08,526 - INFO - Evaluate Summary Time 1.69s\tLoss 1.7032\t Acc@1 45.1700\t Acc@5 88.7100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:07:08,526 - INFO - Head 81.267\tMid 44.725\tTail 9.667\u001b[0m\n",
      "\u001b[32m2024-10-06 15:07:08,526 - INFO - epoch:  49 | train loss: 2.4800 | train accuracy: 91.552 | test loss: 1.7032 | test accuracy: 45.170 | epoch runtime:   4.10 sec | best accuracy: 48.380 @ epoch: 045\u001b[0m\n",
      "\u001b[32m2024-10-06 15:07:12,645 - INFO - Evaluate Summary Time 1.71s\tLoss 1.6599\t Acc@1 46.9000\t Acc@5 89.2600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:07:12,645 - INFO - Head 83.267\tMid 47.300\tTail 10.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:07:12,646 - INFO - epoch:  50 | train loss: 2.4775 | train accuracy: 92.350 | test loss: 1.6599 | test accuracy: 46.900 | epoch runtime:   4.12 sec | best accuracy: 48.380 @ epoch: 045\u001b[0m\n",
      "\u001b[32m2024-10-06 15:07:16,861 - INFO - Evaluate Summary Time 1.80s\tLoss 1.6883\t Acc@1 47.1000\t Acc@5 87.4500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:07:16,862 - INFO - Head 84.333\tMid 46.600\tTail 10.533\u001b[0m\n",
      "\u001b[32m2024-10-06 15:07:16,862 - INFO - epoch:  51 | train loss: 2.4764 | train accuracy: 92.850 | test loss: 1.6883 | test accuracy: 47.100 | epoch runtime:   4.22 sec | best accuracy: 48.380 @ epoch: 045\u001b[0m\n",
      "\u001b[32m2024-10-06 15:07:21,036 - INFO - Evaluate Summary Time 1.71s\tLoss 1.6508\t Acc@1 47.5700\t Acc@5 89.8100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:07:21,037 - INFO - Head 79.167\tMid 50.500\tTail 12.067\u001b[0m\n",
      "\u001b[32m2024-10-06 15:07:21,037 - INFO - epoch:  52 | train loss: 2.4728 | train accuracy: 93.318 | test loss: 1.6508 | test accuracy: 47.570 | epoch runtime:   4.17 sec | best accuracy: 48.380 @ epoch: 045\u001b[0m\n",
      "\u001b[32m2024-10-06 15:07:25,076 - INFO - Evaluate Summary Time 1.64s\tLoss 1.7450\t Acc@1 43.1500\t Acc@5 88.4100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:07:25,077 - INFO - Head 84.200\tMid 38.300\tTail 8.567\u001b[0m\n",
      "\u001b[32m2024-10-06 15:07:25,077 - INFO - epoch:  53 | train loss: 2.4657 | train accuracy: 94.285 | test loss: 1.7450 | test accuracy: 43.150 | epoch runtime:   4.04 sec | best accuracy: 48.380 @ epoch: 045\u001b[0m\n",
      "\u001b[32m2024-10-06 15:07:29,194 - INFO - Evaluate Summary Time 1.68s\tLoss 1.6902\t Acc@1 45.5100\t Acc@5 89.7500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:07:29,194 - INFO - Head 81.700\tMid 43.050\tTail 12.600\u001b[0m\n",
      "\u001b[32m2024-10-06 15:07:29,195 - INFO - epoch:  54 | train loss: 2.4670 | train accuracy: 94.599 | test loss: 1.6902 | test accuracy: 45.510 | epoch runtime:   4.12 sec | best accuracy: 48.380 @ epoch: 045\u001b[0m\n",
      "\u001b[32m2024-10-06 15:07:33,400 - INFO - Evaluate Summary Time 1.79s\tLoss 1.6824\t Acc@1 46.6300\t Acc@5 88.5000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:07:33,400 - INFO - Head 82.867\tMid 46.750\tTail 10.233\u001b[0m\n",
      "\u001b[32m2024-10-06 15:07:33,400 - INFO - epoch:  55 | train loss: 2.4592 | train accuracy: 95.494 | test loss: 1.6824 | test accuracy: 46.630 | epoch runtime:   4.21 sec | best accuracy: 48.380 @ epoch: 045\u001b[0m\n",
      "\u001b[32m2024-10-06 15:07:37,726 - INFO - Evaluate Summary Time 1.87s\tLoss 1.6748\t Acc@1 45.7700\t Acc@5 89.4200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:07:37,727 - INFO - Head 81.467\tMid 40.850\tTail 16.633\u001b[0m\n",
      "\u001b[32m2024-10-06 15:07:37,727 - INFO - epoch:  56 | train loss: 2.4595 | train accuracy: 95.825 | test loss: 1.6748 | test accuracy: 45.770 | epoch runtime:   4.33 sec | best accuracy: 48.380 @ epoch: 045\u001b[0m\n",
      "\u001b[32m2024-10-06 15:07:42,078 - INFO - Evaluate Summary Time 1.82s\tLoss 1.6664\t Acc@1 46.8100\t Acc@5 88.9400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:07:42,078 - INFO - Head 83.533\tMid 44.075\tTail 13.733\u001b[0m\n",
      "\u001b[32m2024-10-06 15:07:42,078 - INFO - epoch:  57 | train loss: 2.4566 | train accuracy: 96.099 | test loss: 1.6664 | test accuracy: 46.810 | epoch runtime:   4.35 sec | best accuracy: 48.380 @ epoch: 045\u001b[0m\n",
      "\u001b[32m2024-10-06 15:07:46,343 - INFO - Evaluate Summary Time 1.76s\tLoss 1.6414\t Acc@1 47.2400\t Acc@5 89.2600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:07:46,344 - INFO - Head 78.433\tMid 46.700\tTail 16.767\u001b[0m\n",
      "\u001b[32m2024-10-06 15:07:46,344 - INFO - epoch:  58 | train loss: 2.4532 | train accuracy: 96.526 | test loss: 1.6414 | test accuracy: 47.240 | epoch runtime:   4.27 sec | best accuracy: 48.380 @ epoch: 045\u001b[0m\n",
      "\u001b[32m2024-10-06 15:07:50,416 - INFO - Evaluate Summary Time 1.70s\tLoss 1.7057\t Acc@1 45.2800\t Acc@5 88.0800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:07:50,416 - INFO - Head 81.833\tMid 43.475\tTail 11.133\u001b[0m\n",
      "\u001b[32m2024-10-06 15:07:50,417 - INFO - epoch:  59 | train loss: 2.4524 | train accuracy: 96.905 | test loss: 1.7057 | test accuracy: 45.280 | epoch runtime:   4.07 sec | best accuracy: 48.380 @ epoch: 045\u001b[0m\n",
      "\u001b[32m2024-10-06 15:07:54,645 - INFO - Evaluate Summary Time 1.78s\tLoss 1.6498\t Acc@1 47.4500\t Acc@5 89.0600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:07:54,646 - INFO - Head 83.100\tMid 46.000\tTail 13.733\u001b[0m\n",
      "\u001b[32m2024-10-06 15:07:54,646 - INFO - epoch:  60 | train loss: 2.4506 | train accuracy: 96.993 | test loss: 1.6498 | test accuracy: 47.450 | epoch runtime:   4.23 sec | best accuracy: 48.380 @ epoch: 045\u001b[0m\n",
      "\u001b[32m2024-10-06 15:07:58,783 - INFO - Evaluate Summary Time 1.70s\tLoss 1.6722\t Acc@1 45.8500\t Acc@5 88.3300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:07:58,784 - INFO - Head 83.867\tMid 39.725\tTail 16.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:07:58,784 - INFO - epoch:  61 | train loss: 2.4489 | train accuracy: 97.356 | test loss: 1.6722 | test accuracy: 45.850 | epoch runtime:   4.14 sec | best accuracy: 48.380 @ epoch: 045\u001b[0m\n",
      "\u001b[32m2024-10-06 15:08:02,843 - INFO - Evaluate Summary Time 1.66s\tLoss 1.7205\t Acc@1 44.4500\t Acc@5 88.3100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:08:02,843 - INFO - Head 81.833\tMid 40.350\tTail 12.533\u001b[0m\n",
      "\u001b[32m2024-10-06 15:08:02,843 - INFO - epoch:  62 | train loss: 2.4445 | train accuracy: 97.582 | test loss: 1.7205 | test accuracy: 44.450 | epoch runtime:   4.06 sec | best accuracy: 48.380 @ epoch: 045\u001b[0m\n",
      "\u001b[32m2024-10-06 15:08:07,071 - INFO - Evaluate Summary Time 1.77s\tLoss 1.6943\t Acc@1 45.6900\t Acc@5 87.6600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:08:07,072 - INFO - Head 83.700\tMid 42.750\tTail 11.600\u001b[0m\n",
      "\u001b[32m2024-10-06 15:08:07,072 - INFO - epoch:  63 | train loss: 2.4452 | train accuracy: 97.711 | test loss: 1.6943 | test accuracy: 45.690 | epoch runtime:   4.23 sec | best accuracy: 48.380 @ epoch: 045\u001b[0m\n",
      "\u001b[32m2024-10-06 15:08:11,186 - INFO - Evaluate Summary Time 1.74s\tLoss 1.6617\t Acc@1 46.9600\t Acc@5 88.6900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:08:11,186 - INFO - Head 82.367\tMid 46.475\tTail 12.200\u001b[0m\n",
      "\u001b[32m2024-10-06 15:08:11,187 - INFO - epoch:  64 | train loss: 2.4437 | train accuracy: 97.920 | test loss: 1.6617 | test accuracy: 46.960 | epoch runtime:   4.11 sec | best accuracy: 48.380 @ epoch: 045\u001b[0m\n",
      "\u001b[32m2024-10-06 15:08:15,340 - INFO - Evaluate Summary Time 1.73s\tLoss 1.7202\t Acc@1 44.0100\t Acc@5 88.5100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:08:15,341 - INFO - Head 78.867\tMid 40.950\tTail 13.233\u001b[0m\n",
      "\u001b[32m2024-10-06 15:08:15,341 - INFO - epoch:  65 | train loss: 2.4412 | train accuracy: 97.936 | test loss: 1.7202 | test accuracy: 44.010 | epoch runtime:   4.15 sec | best accuracy: 48.380 @ epoch: 045\u001b[0m\n",
      "\u001b[32m2024-10-06 15:08:19,399 - INFO - Evaluate Summary Time 1.68s\tLoss 1.6674\t Acc@1 46.1100\t Acc@5 89.5000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:08:19,400 - INFO - Head 80.700\tMid 44.800\tTail 13.267\u001b[0m\n",
      "\u001b[32m2024-10-06 15:08:19,400 - INFO - epoch:  66 | train loss: 2.4388 | train accuracy: 98.041 | test loss: 1.6674 | test accuracy: 46.110 | epoch runtime:   4.06 sec | best accuracy: 48.380 @ epoch: 045\u001b[0m\n",
      "\u001b[32m2024-10-06 15:08:23,660 - INFO - Evaluate Summary Time 1.83s\tLoss 1.6862\t Acc@1 46.1700\t Acc@5 88.4600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:08:23,660 - INFO - Head 82.933\tMid 43.075\tTail 13.533\u001b[0m\n",
      "\u001b[32m2024-10-06 15:08:23,661 - INFO - epoch:  67 | train loss: 2.4383 | train accuracy: 98.235 | test loss: 1.6862 | test accuracy: 46.170 | epoch runtime:   4.26 sec | best accuracy: 48.380 @ epoch: 045\u001b[0m\n",
      "\u001b[32m2024-10-06 15:08:27,769 - INFO - Evaluate Summary Time 1.67s\tLoss 1.6524\t Acc@1 47.7300\t Acc@5 88.1700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:08:27,770 - INFO - Head 82.700\tMid 46.875\tTail 13.900\u001b[0m\n",
      "\u001b[32m2024-10-06 15:08:27,770 - INFO - epoch:  68 | train loss: 2.4365 | train accuracy: 98.477 | test loss: 1.6524 | test accuracy: 47.730 | epoch runtime:   4.11 sec | best accuracy: 48.380 @ epoch: 045\u001b[0m\n",
      "\u001b[32m2024-10-06 15:08:31,804 - INFO - Evaluate Summary Time 1.65s\tLoss 1.7162\t Acc@1 43.9800\t Acc@5 87.8100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:08:31,805 - INFO - Head 82.200\tMid 36.600\tTail 15.600\u001b[0m\n",
      "\u001b[32m2024-10-06 15:08:31,805 - INFO - epoch:  69 | train loss: 2.4340 | train accuracy: 98.477 | test loss: 1.7162 | test accuracy: 43.980 | epoch runtime:   4.04 sec | best accuracy: 48.380 @ epoch: 045\u001b[0m\n",
      "\u001b[32m2024-10-06 15:08:36,079 - INFO - Evaluate Summary Time 1.80s\tLoss 1.6764\t Acc@1 46.5400\t Acc@5 87.4900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:08:36,079 - INFO - Head 84.100\tMid 43.650\tTail 12.833\u001b[0m\n",
      "\u001b[32m2024-10-06 15:08:36,080 - INFO - epoch:  70 | train loss: 2.4352 | train accuracy: 98.622 | test loss: 1.6764 | test accuracy: 46.540 | epoch runtime:   4.27 sec | best accuracy: 48.380 @ epoch: 045\u001b[0m\n",
      "\u001b[32m2024-10-06 15:08:40,382 - INFO - Evaluate Summary Time 1.82s\tLoss 1.6961\t Acc@1 45.4300\t Acc@5 87.8400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:08:40,382 - INFO - Head 82.100\tMid 41.450\tTail 14.067\u001b[0m\n",
      "\u001b[32m2024-10-06 15:08:40,383 - INFO - epoch:  71 | train loss: 2.4336 | train accuracy: 98.581 | test loss: 1.6961 | test accuracy: 45.430 | epoch runtime:   4.30 sec | best accuracy: 48.380 @ epoch: 045\u001b[0m\n",
      "\u001b[32m2024-10-06 15:08:44,621 - INFO - Evaluate Summary Time 1.69s\tLoss 1.7085\t Acc@1 45.2600\t Acc@5 86.9300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:08:44,622 - INFO - Head 82.900\tMid 40.900\tTail 13.433\u001b[0m\n",
      "\u001b[32m2024-10-06 15:08:44,622 - INFO - epoch:  72 | train loss: 2.4325 | train accuracy: 98.597 | test loss: 1.7085 | test accuracy: 45.260 | epoch runtime:   4.24 sec | best accuracy: 48.380 @ epoch: 045\u001b[0m\n",
      "\u001b[32m2024-10-06 15:08:48,722 - INFO - Evaluate Summary Time 1.67s\tLoss 1.7108\t Acc@1 44.5400\t Acc@5 87.8900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:08:48,722 - INFO - Head 81.867\tMid 39.825\tTail 13.500\u001b[0m\n",
      "\u001b[32m2024-10-06 15:08:48,723 - INFO - epoch:  73 | train loss: 2.4293 | train accuracy: 98.791 | test loss: 1.7108 | test accuracy: 44.540 | epoch runtime:   4.10 sec | best accuracy: 48.380 @ epoch: 045\u001b[0m\n",
      "\u001b[32m2024-10-06 15:08:52,842 - INFO - Evaluate Summary Time 1.74s\tLoss 1.6736\t Acc@1 46.4600\t Acc@5 88.3500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:08:52,842 - INFO - Head 82.167\tMid 42.425\tTail 16.133\u001b[0m\n",
      "\u001b[32m2024-10-06 15:08:52,842 - INFO - epoch:  74 | train loss: 2.4305 | train accuracy: 98.799 | test loss: 1.6736 | test accuracy: 46.460 | epoch runtime:   4.12 sec | best accuracy: 48.380 @ epoch: 045\u001b[0m\n",
      "\u001b[32m2024-10-06 15:08:56,872 - INFO - Evaluate Summary Time 1.63s\tLoss 1.6563\t Acc@1 47.4300\t Acc@5 88.8200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:08:56,873 - INFO - Head 83.000\tMid 44.600\tTail 15.633\u001b[0m\n",
      "\u001b[32m2024-10-06 15:08:56,873 - INFO - epoch:  75 | train loss: 2.4248 | train accuracy: 98.831 | test loss: 1.6563 | test accuracy: 47.430 | epoch runtime:   4.03 sec | best accuracy: 48.380 @ epoch: 045\u001b[0m\n",
      "\u001b[32m2024-10-06 15:09:01,082 - INFO - Evaluate Summary Time 1.73s\tLoss 1.6988\t Acc@1 45.1000\t Acc@5 87.5400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:09:01,083 - INFO - Head 83.033\tMid 38.850\tTail 15.500\u001b[0m\n",
      "\u001b[32m2024-10-06 15:09:01,083 - INFO - epoch:  76 | train loss: 2.4290 | train accuracy: 98.896 | test loss: 1.6988 | test accuracy: 45.100 | epoch runtime:   4.21 sec | best accuracy: 48.380 @ epoch: 045\u001b[0m\n",
      "\u001b[32m2024-10-06 15:09:05,276 - INFO - Evaluate Summary Time 1.74s\tLoss 1.7240\t Acc@1 45.1500\t Acc@5 86.7300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:09:05,277 - INFO - Head 84.733\tMid 37.800\tTail 15.367\u001b[0m\n",
      "\u001b[32m2024-10-06 15:09:05,277 - INFO - epoch:  77 | train loss: 2.4287 | train accuracy: 98.960 | test loss: 1.7240 | test accuracy: 45.150 | epoch runtime:   4.19 sec | best accuracy: 48.380 @ epoch: 045\u001b[0m\n",
      "\u001b[32m2024-10-06 15:09:09,403 - INFO - Evaluate Summary Time 1.65s\tLoss 1.6472\t Acc@1 47.8300\t Acc@5 89.1300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:09:09,403 - INFO - Head 82.300\tMid 45.300\tTail 16.733\u001b[0m\n",
      "\u001b[32m2024-10-06 15:09:09,404 - INFO - epoch:  78 | train loss: 2.4265 | train accuracy: 99.049 | test loss: 1.6472 | test accuracy: 47.830 | epoch runtime:   4.13 sec | best accuracy: 48.380 @ epoch: 045\u001b[0m\n",
      "\u001b[32m2024-10-06 15:09:13,537 - INFO - Evaluate Summary Time 1.75s\tLoss 1.6541\t Acc@1 47.8400\t Acc@5 88.7900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:09:13,538 - INFO - Head 83.033\tMid 44.325\tTail 17.333\u001b[0m\n",
      "\u001b[32m2024-10-06 15:09:13,538 - INFO - epoch:  79 | train loss: 2.4269 | train accuracy: 99.057 | test loss: 1.6541 | test accuracy: 47.840 | epoch runtime:   4.13 sec | best accuracy: 48.380 @ epoch: 045\u001b[0m\n",
      "\u001b[32m2024-10-06 15:09:17,724 - INFO - Evaluate Summary Time 1.76s\tLoss 1.7221\t Acc@1 45.1700\t Acc@5 87.7200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:09:17,725 - INFO - Head 82.433\tMid 41.625\tTail 12.633\u001b[0m\n",
      "\u001b[32m2024-10-06 15:09:17,725 - INFO - epoch:  80 | train loss: 2.4239 | train accuracy: 99.065 | test loss: 1.7221 | test accuracy: 45.170 | epoch runtime:   4.19 sec | best accuracy: 48.380 @ epoch: 045\u001b[0m\n",
      "\u001b[32m2024-10-06 15:09:21,758 - INFO - Evaluate Summary Time 1.61s\tLoss 1.6720\t Acc@1 46.9600\t Acc@5 88.8000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:09:21,758 - INFO - Head 82.700\tMid 44.350\tTail 14.700\u001b[0m\n",
      "\u001b[32m2024-10-06 15:09:21,759 - INFO - epoch:  81 | train loss: 2.4139 | train accuracy: 99.315 | test loss: 1.6720 | test accuracy: 46.960 | epoch runtime:   4.03 sec | best accuracy: 48.380 @ epoch: 045\u001b[0m\n",
      "\u001b[32m2024-10-06 15:09:25,907 - INFO - Evaluate Summary Time 1.72s\tLoss 1.6672\t Acc@1 47.5100\t Acc@5 89.0700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:09:25,907 - INFO - Head 81.167\tMid 45.525\tTail 16.500\u001b[0m\n",
      "\u001b[32m2024-10-06 15:09:25,908 - INFO - epoch:  82 | train loss: 2.4085 | train accuracy: 99.460 | test loss: 1.6672 | test accuracy: 47.510 | epoch runtime:   4.15 sec | best accuracy: 48.380 @ epoch: 045\u001b[0m\n",
      "\u001b[32m2024-10-06 15:09:29,939 - INFO - Evaluate Summary Time 1.68s\tLoss 1.6711\t Acc@1 46.8900\t Acc@5 89.2500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:09:29,940 - INFO - Head 82.267\tMid 43.300\tTail 16.300\u001b[0m\n",
      "\u001b[32m2024-10-06 15:09:29,940 - INFO - epoch:  83 | train loss: 2.4105 | train accuracy: 99.565 | test loss: 1.6711 | test accuracy: 46.890 | epoch runtime:   4.03 sec | best accuracy: 48.380 @ epoch: 045\u001b[0m\n",
      "\u001b[32m2024-10-06 15:09:34,102 - INFO - Evaluate Summary Time 1.75s\tLoss 1.6522\t Acc@1 48.1400\t Acc@5 88.8500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:09:34,102 - INFO - Head 83.233\tMid 44.450\tTail 17.967\u001b[0m\n",
      "\u001b[32m2024-10-06 15:09:34,102 - INFO - epoch:  84 | train loss: 2.4079 | train accuracy: 99.573 | test loss: 1.6522 | test accuracy: 48.140 | epoch runtime:   4.16 sec | best accuracy: 48.380 @ epoch: 045\u001b[0m\n",
      "\u001b[32m2024-10-06 15:09:38,456 - INFO - Evaluate Summary Time 1.85s\tLoss 1.6518\t Acc@1 48.5800\t Acc@5 87.9300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:09:38,456 - INFO - Head 83.333\tMid 46.600\tTail 16.467\u001b[0m\n",
      "\u001b[32m2024-10-06 15:09:38,457 - INFO - epoch:  85 | train loss: 2.4095 | train accuracy: 99.613 | test loss: 1.6518 | test accuracy: 48.580 | epoch runtime:   4.35 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:09:42,838 - INFO - Evaluate Summary Time 1.86s\tLoss 1.6672\t Acc@1 47.1000\t Acc@5 88.4000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:09:42,839 - INFO - Head 83.500\tMid 41.875\tTail 17.667\u001b[0m\n",
      "\u001b[32m2024-10-06 15:09:42,839 - INFO - epoch:  86 | train loss: 2.4056 | train accuracy: 99.589 | test loss: 1.6672 | test accuracy: 47.100 | epoch runtime:   4.38 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:09:47,056 - INFO - Evaluate Summary Time 1.75s\tLoss 1.6552\t Acc@1 47.7800\t Acc@5 88.7500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:09:47,056 - INFO - Head 81.767\tMid 46.275\tTail 15.800\u001b[0m\n",
      "\u001b[32m2024-10-06 15:09:47,057 - INFO - epoch:  87 | train loss: 2.4056 | train accuracy: 99.581 | test loss: 1.6552 | test accuracy: 47.780 | epoch runtime:   4.22 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:09:51,198 - INFO - Evaluate Summary Time 1.72s\tLoss 1.6601\t Acc@1 47.4100\t Acc@5 88.6300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:09:51,198 - INFO - Head 84.367\tMid 43.800\tTail 15.267\u001b[0m\n",
      "\u001b[32m2024-10-06 15:09:51,198 - INFO - epoch:  88 | train loss: 2.4052 | train accuracy: 99.621 | test loss: 1.6601 | test accuracy: 47.410 | epoch runtime:   4.14 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:09:55,319 - INFO - Evaluate Summary Time 1.69s\tLoss 1.6764\t Acc@1 46.7700\t Acc@5 88.0900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:09:55,320 - INFO - Head 82.900\tMid 42.550\tTail 16.267\u001b[0m\n",
      "\u001b[32m2024-10-06 15:09:55,320 - INFO - epoch:  89 | train loss: 2.4046 | train accuracy: 99.653 | test loss: 1.6764 | test accuracy: 46.770 | epoch runtime:   4.12 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:09:59,337 - INFO - Evaluate Summary Time 1.71s\tLoss 1.6864\t Acc@1 46.3200\t Acc@5 87.8800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:09:59,337 - INFO - Head 83.200\tMid 41.350\tTail 16.067\u001b[0m\n",
      "\u001b[32m2024-10-06 15:09:59,338 - INFO - epoch:  90 | train loss: 2.4043 | train accuracy: 99.670 | test loss: 1.6864 | test accuracy: 46.320 | epoch runtime:   4.02 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:10:03,486 - INFO - Evaluate Summary Time 1.75s\tLoss 1.6721\t Acc@1 46.6700\t Acc@5 88.9600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:10:03,487 - INFO - Head 83.600\tMid 41.825\tTail 16.200\u001b[0m\n",
      "\u001b[32m2024-10-06 15:10:03,487 - INFO - epoch:  91 | train loss: 2.4028 | train accuracy: 99.686 | test loss: 1.6721 | test accuracy: 46.670 | epoch runtime:   4.15 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:10:07,742 - INFO - Evaluate Summary Time 1.81s\tLoss 1.6794\t Acc@1 47.0600\t Acc@5 88.7000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:10:07,742 - INFO - Head 83.433\tMid 44.525\tTail 14.067\u001b[0m\n",
      "\u001b[32m2024-10-06 15:10:07,743 - INFO - epoch:  92 | train loss: 2.4027 | train accuracy: 99.637 | test loss: 1.6794 | test accuracy: 47.060 | epoch runtime:   4.26 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:10:11,866 - INFO - Evaluate Summary Time 1.72s\tLoss 1.6672\t Acc@1 47.0200\t Acc@5 88.6900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:10:11,866 - INFO - Head 82.667\tMid 43.600\tTail 15.933\u001b[0m\n",
      "\u001b[32m2024-10-06 15:10:11,866 - INFO - epoch:  93 | train loss: 2.4034 | train accuracy: 99.718 | test loss: 1.6672 | test accuracy: 47.020 | epoch runtime:   4.12 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:10:16,017 - INFO - Evaluate Summary Time 1.73s\tLoss 1.6914\t Acc@1 46.6000\t Acc@5 87.9400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:10:16,017 - INFO - Head 84.467\tMid 41.825\tTail 15.100\u001b[0m\n",
      "\u001b[32m2024-10-06 15:10:16,017 - INFO - epoch:  94 | train loss: 2.4029 | train accuracy: 99.670 | test loss: 1.6914 | test accuracy: 46.600 | epoch runtime:   4.15 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:10:20,133 - INFO - Evaluate Summary Time 1.75s\tLoss 1.7161\t Acc@1 45.2800\t Acc@5 87.7100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:10:20,134 - INFO - Head 82.167\tMid 41.225\tTail 13.800\u001b[0m\n",
      "\u001b[32m2024-10-06 15:10:20,134 - INFO - epoch:  95 | train loss: 2.4008 | train accuracy: 99.678 | test loss: 1.7161 | test accuracy: 45.280 | epoch runtime:   4.12 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:10:24,205 - INFO - Evaluate Summary Time 1.74s\tLoss 1.6812\t Acc@1 46.7000\t Acc@5 88.7300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:10:24,206 - INFO - Head 82.467\tMid 42.775\tTail 16.167\u001b[0m\n",
      "\u001b[32m2024-10-06 15:10:24,206 - INFO - epoch:  96 | train loss: 2.4003 | train accuracy: 99.790 | test loss: 1.6812 | test accuracy: 46.700 | epoch runtime:   4.07 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:10:28,278 - INFO - Evaluate Summary Time 1.68s\tLoss 1.6720\t Acc@1 47.3800\t Acc@5 88.5500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:10:28,278 - INFO - Head 83.300\tMid 45.425\tTail 14.067\u001b[0m\n",
      "\u001b[32m2024-10-06 15:10:28,279 - INFO - epoch:  97 | train loss: 2.4001 | train accuracy: 99.750 | test loss: 1.6720 | test accuracy: 47.380 | epoch runtime:   4.07 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:10:32,506 - INFO - Evaluate Summary Time 1.78s\tLoss 1.6732\t Acc@1 47.5900\t Acc@5 88.0200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:10:32,506 - INFO - Head 83.867\tMid 44.775\tTail 15.067\u001b[0m\n",
      "\u001b[32m2024-10-06 15:10:32,506 - INFO - epoch:  98 | train loss: 2.4016 | train accuracy: 99.782 | test loss: 1.6732 | test accuracy: 47.590 | epoch runtime:   4.23 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:10:36,833 - INFO - Evaluate Summary Time 1.82s\tLoss 1.6827\t Acc@1 46.7800\t Acc@5 88.8500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:10:36,834 - INFO - Head 83.033\tMid 41.850\tTail 17.100\u001b[0m\n",
      "\u001b[32m2024-10-06 15:10:36,834 - INFO - epoch:  99 | train loss: 2.4010 | train accuracy: 99.742 | test loss: 1.6827 | test accuracy: 46.780 | epoch runtime:   4.33 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:10:41,104 - INFO - Evaluate Summary Time 1.77s\tLoss 1.6794\t Acc@1 47.0500\t Acc@5 88.2000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:10:41,104 - INFO - Head 83.933\tMid 41.600\tTail 17.433\u001b[0m\n",
      "\u001b[32m2024-10-06 15:10:41,105 - INFO - epoch: 100 | train loss: 2.4004 | train accuracy: 99.726 | test loss: 1.6794 | test accuracy: 47.050 | epoch runtime:   4.27 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:10:45,364 - INFO - Evaluate Summary Time 1.68s\tLoss 1.6669\t Acc@1 47.3100\t Acc@5 88.3400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:10:45,365 - INFO - Head 83.400\tMid 42.525\tTail 17.600\u001b[0m\n",
      "\u001b[32m2024-10-06 15:10:45,365 - INFO - epoch: 101 | train loss: 2.3974 | train accuracy: 99.782 | test loss: 1.6669 | test accuracy: 47.310 | epoch runtime:   4.26 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:10:49,520 - INFO - Evaluate Summary Time 1.73s\tLoss 1.6649\t Acc@1 47.7700\t Acc@5 89.4800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:10:49,520 - INFO - Head 83.200\tMid 44.325\tTail 16.933\u001b[0m\n",
      "\u001b[32m2024-10-06 15:10:49,521 - INFO - epoch: 102 | train loss: 2.3975 | train accuracy: 99.807 | test loss: 1.6649 | test accuracy: 47.770 | epoch runtime:   4.16 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:10:53,664 - INFO - Evaluate Summary Time 1.78s\tLoss 1.6862\t Acc@1 46.9000\t Acc@5 88.4100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:10:53,665 - INFO - Head 82.433\tMid 43.000\tTail 16.567\u001b[0m\n",
      "\u001b[32m2024-10-06 15:10:53,665 - INFO - epoch: 103 | train loss: 2.3987 | train accuracy: 99.798 | test loss: 1.6862 | test accuracy: 46.900 | epoch runtime:   4.14 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:10:57,756 - INFO - Evaluate Summary Time 1.69s\tLoss 1.6977\t Acc@1 46.1600\t Acc@5 88.1200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:10:57,756 - INFO - Head 82.900\tMid 42.875\tTail 13.800\u001b[0m\n",
      "\u001b[32m2024-10-06 15:10:57,757 - INFO - epoch: 104 | train loss: 2.3966 | train accuracy: 99.734 | test loss: 1.6977 | test accuracy: 46.160 | epoch runtime:   4.09 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:11:01,912 - INFO - Evaluate Summary Time 1.73s\tLoss 1.7067\t Acc@1 45.8000\t Acc@5 88.2900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:11:01,912 - INFO - Head 82.367\tMid 40.400\tTail 16.433\u001b[0m\n",
      "\u001b[32m2024-10-06 15:11:01,912 - INFO - epoch: 105 | train loss: 2.3982 | train accuracy: 99.831 | test loss: 1.7067 | test accuracy: 45.800 | epoch runtime:   4.16 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:11:06,097 - INFO - Evaluate Summary Time 1.71s\tLoss 1.6953\t Acc@1 46.4900\t Acc@5 88.0300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:11:06,098 - INFO - Head 82.367\tMid 43.350\tTail 14.800\u001b[0m\n",
      "\u001b[32m2024-10-06 15:11:06,098 - INFO - epoch: 106 | train loss: 2.3963 | train accuracy: 99.798 | test loss: 1.6953 | test accuracy: 46.490 | epoch runtime:   4.19 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:11:10,310 - INFO - Evaluate Summary Time 1.78s\tLoss 1.6778\t Acc@1 47.2400\t Acc@5 88.3800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:11:10,310 - INFO - Head 83.800\tMid 42.525\tTail 16.967\u001b[0m\n",
      "\u001b[32m2024-10-06 15:11:10,310 - INFO - epoch: 107 | train loss: 2.3971 | train accuracy: 99.790 | test loss: 1.6778 | test accuracy: 47.240 | epoch runtime:   4.21 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:11:14,373 - INFO - Evaluate Summary Time 1.69s\tLoss 1.6724\t Acc@1 47.3300\t Acc@5 88.7600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:11:14,374 - INFO - Head 82.767\tMid 44.350\tTail 15.867\u001b[0m\n",
      "\u001b[32m2024-10-06 15:11:14,374 - INFO - epoch: 108 | train loss: 2.3963 | train accuracy: 99.798 | test loss: 1.6724 | test accuracy: 47.330 | epoch runtime:   4.06 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:11:18,554 - INFO - Evaluate Summary Time 1.75s\tLoss 1.6841\t Acc@1 47.0100\t Acc@5 88.5800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:11:18,555 - INFO - Head 81.500\tMid 43.850\tTail 16.733\u001b[0m\n",
      "\u001b[32m2024-10-06 15:11:18,555 - INFO - epoch: 109 | train loss: 2.3946 | train accuracy: 99.807 | test loss: 1.6841 | test accuracy: 47.010 | epoch runtime:   4.18 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:11:22,640 - INFO - Evaluate Summary Time 1.69s\tLoss 1.6668\t Acc@1 48.0100\t Acc@5 88.3300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:11:22,641 - INFO - Head 84.000\tMid 45.400\tTail 15.500\u001b[0m\n",
      "\u001b[32m2024-10-06 15:11:22,641 - INFO - epoch: 110 | train loss: 2.3949 | train accuracy: 99.855 | test loss: 1.6668 | test accuracy: 48.010 | epoch runtime:   4.09 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:11:26,825 - INFO - Evaluate Summary Time 1.71s\tLoss 1.6734\t Acc@1 47.3600\t Acc@5 88.5000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:11:26,826 - INFO - Head 83.033\tMid 44.725\tTail 15.200\u001b[0m\n",
      "\u001b[32m2024-10-06 15:11:26,826 - INFO - epoch: 111 | train loss: 2.3955 | train accuracy: 99.823 | test loss: 1.6734 | test accuracy: 47.360 | epoch runtime:   4.19 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:11:30,847 - INFO - Evaluate Summary Time 1.63s\tLoss 1.6776\t Acc@1 47.4700\t Acc@5 88.5100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:11:30,847 - INFO - Head 83.933\tMid 43.500\tTail 16.300\u001b[0m\n",
      "\u001b[32m2024-10-06 15:11:30,848 - INFO - epoch: 112 | train loss: 2.3941 | train accuracy: 99.823 | test loss: 1.6776 | test accuracy: 47.470 | epoch runtime:   4.02 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:11:35,051 - INFO - Evaluate Summary Time 1.75s\tLoss 1.6939\t Acc@1 46.3300\t Acc@5 88.3800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:11:35,052 - INFO - Head 83.000\tMid 42.525\tTail 14.733\u001b[0m\n",
      "\u001b[32m2024-10-06 15:11:35,052 - INFO - epoch: 113 | train loss: 2.3949 | train accuracy: 99.807 | test loss: 1.6939 | test accuracy: 46.330 | epoch runtime:   4.20 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:11:39,414 - INFO - Evaluate Summary Time 1.70s\tLoss 1.6902\t Acc@1 46.5900\t Acc@5 88.5400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:11:39,414 - INFO - Head 82.900\tMid 43.075\tTail 14.967\u001b[0m\n",
      "\u001b[32m2024-10-06 15:11:39,414 - INFO - epoch: 114 | train loss: 2.3944 | train accuracy: 99.815 | test loss: 1.6902 | test accuracy: 46.590 | epoch runtime:   4.36 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:11:43,888 - INFO - Evaluate Summary Time 1.74s\tLoss 1.6795\t Acc@1 47.6400\t Acc@5 88.3800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:11:43,889 - INFO - Head 83.700\tMid 44.550\tTail 15.700\u001b[0m\n",
      "\u001b[32m2024-10-06 15:11:43,889 - INFO - epoch: 115 | train loss: 2.3952 | train accuracy: 99.847 | test loss: 1.6795 | test accuracy: 47.640 | epoch runtime:   4.47 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:11:48,155 - INFO - Evaluate Summary Time 1.71s\tLoss 1.6800\t Acc@1 47.5400\t Acc@5 88.2900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:11:48,156 - INFO - Head 83.667\tMid 44.325\tTail 15.700\u001b[0m\n",
      "\u001b[32m2024-10-06 15:11:48,156 - INFO - epoch: 116 | train loss: 2.3927 | train accuracy: 99.807 | test loss: 1.6800 | test accuracy: 47.540 | epoch runtime:   4.27 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:11:52,567 - INFO - Evaluate Summary Time 1.71s\tLoss 1.6930\t Acc@1 46.4400\t Acc@5 88.5300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:11:52,567 - INFO - Head 82.633\tMid 42.375\tTail 15.667\u001b[0m\n",
      "\u001b[32m2024-10-06 15:11:52,567 - INFO - epoch: 117 | train loss: 2.3951 | train accuracy: 99.798 | test loss: 1.6930 | test accuracy: 46.440 | epoch runtime:   4.41 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:11:56,778 - INFO - Evaluate Summary Time 1.79s\tLoss 1.6811\t Acc@1 47.0300\t Acc@5 88.6400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:11:56,778 - INFO - Head 83.333\tMid 43.600\tTail 15.300\u001b[0m\n",
      "\u001b[32m2024-10-06 15:11:56,779 - INFO - epoch: 118 | train loss: 2.3921 | train accuracy: 99.839 | test loss: 1.6811 | test accuracy: 47.030 | epoch runtime:   4.21 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:12:00,840 - INFO - Evaluate Summary Time 1.67s\tLoss 1.6900\t Acc@1 46.8900\t Acc@5 88.3000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:12:00,841 - INFO - Head 82.467\tMid 42.750\tTail 16.833\u001b[0m\n",
      "\u001b[32m2024-10-06 15:12:00,841 - INFO - epoch: 119 | train loss: 2.3923 | train accuracy: 99.855 | test loss: 1.6900 | test accuracy: 46.890 | epoch runtime:   4.06 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:12:04,953 - INFO - Evaluate Summary Time 1.66s\tLoss 1.7049\t Acc@1 46.3500\t Acc@5 87.7900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:12:04,954 - INFO - Head 83.567\tMid 42.025\tTail 14.900\u001b[0m\n",
      "\u001b[32m2024-10-06 15:12:04,954 - INFO - epoch: 120 | train loss: 2.3921 | train accuracy: 99.847 | test loss: 1.7049 | test accuracy: 46.350 | epoch runtime:   4.11 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:12:09,167 - INFO - Evaluate Summary Time 1.77s\tLoss 1.6949\t Acc@1 46.7100\t Acc@5 87.8600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:12:09,168 - INFO - Head 84.133\tMid 42.625\tTail 14.733\u001b[0m\n",
      "\u001b[32m2024-10-06 15:12:09,168 - INFO - epoch: 121 | train loss: 2.3943 | train accuracy: 99.871 | test loss: 1.6949 | test accuracy: 46.710 | epoch runtime:   4.21 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:12:13,279 - INFO - Evaluate Summary Time 1.72s\tLoss 1.7014\t Acc@1 46.2600\t Acc@5 88.3900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:12:13,279 - INFO - Head 82.333\tMid 42.675\tTail 14.967\u001b[0m\n",
      "\u001b[32m2024-10-06 15:12:13,279 - INFO - epoch: 122 | train loss: 2.3906 | train accuracy: 99.855 | test loss: 1.7014 | test accuracy: 46.260 | epoch runtime:   4.11 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:12:17,335 - INFO - Evaluate Summary Time 1.63s\tLoss 1.6840\t Acc@1 47.0900\t Acc@5 88.2600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:12:17,335 - INFO - Head 83.800\tMid 43.800\tTail 14.767\u001b[0m\n",
      "\u001b[32m2024-10-06 15:12:17,335 - INFO - epoch: 123 | train loss: 2.3931 | train accuracy: 99.831 | test loss: 1.6840 | test accuracy: 47.090 | epoch runtime:   4.06 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:12:21,478 - INFO - Evaluate Summary Time 1.74s\tLoss 1.6950\t Acc@1 46.6400\t Acc@5 87.8200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:12:21,478 - INFO - Head 82.900\tMid 43.350\tTail 14.767\u001b[0m\n",
      "\u001b[32m2024-10-06 15:12:21,479 - INFO - epoch: 124 | train loss: 2.3913 | train accuracy: 99.863 | test loss: 1.6950 | test accuracy: 46.640 | epoch runtime:   4.14 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:12:25,603 - INFO - Evaluate Summary Time 1.69s\tLoss 1.6751\t Acc@1 47.3000\t Acc@5 88.8300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:12:25,603 - INFO - Head 82.833\tMid 44.500\tTail 15.500\u001b[0m\n",
      "\u001b[32m2024-10-06 15:12:25,603 - INFO - epoch: 125 | train loss: 2.3929 | train accuracy: 99.847 | test loss: 1.6751 | test accuracy: 47.300 | epoch runtime:   4.12 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:12:29,773 - INFO - Evaluate Summary Time 1.75s\tLoss 1.6806\t Acc@1 47.5500\t Acc@5 88.4100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:12:29,773 - INFO - Head 83.133\tMid 44.550\tTail 15.967\u001b[0m\n",
      "\u001b[32m2024-10-06 15:12:29,774 - INFO - epoch: 126 | train loss: 2.3901 | train accuracy: 99.855 | test loss: 1.6806 | test accuracy: 47.550 | epoch runtime:   4.17 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:12:33,915 - INFO - Evaluate Summary Time 1.73s\tLoss 1.7008\t Acc@1 46.4100\t Acc@5 88.1300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:12:33,915 - INFO - Head 83.467\tMid 42.275\tTail 14.867\u001b[0m\n",
      "\u001b[32m2024-10-06 15:12:33,916 - INFO - epoch: 127 | train loss: 2.3911 | train accuracy: 99.863 | test loss: 1.7008 | test accuracy: 46.410 | epoch runtime:   4.14 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:12:38,176 - INFO - Evaluate Summary Time 1.78s\tLoss 1.6817\t Acc@1 47.1100\t Acc@5 88.6600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:12:38,177 - INFO - Head 82.233\tMid 43.525\tTail 16.767\u001b[0m\n",
      "\u001b[32m2024-10-06 15:12:38,177 - INFO - epoch: 128 | train loss: 2.3917 | train accuracy: 99.871 | test loss: 1.6817 | test accuracy: 47.110 | epoch runtime:   4.26 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:12:42,461 - INFO - Evaluate Summary Time 1.78s\tLoss 1.6823\t Acc@1 47.5100\t Acc@5 88.4000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:12:42,462 - INFO - Head 83.533\tMid 44.300\tTail 15.767\u001b[0m\n",
      "\u001b[32m2024-10-06 15:12:42,462 - INFO - epoch: 129 | train loss: 2.3886 | train accuracy: 99.863 | test loss: 1.6823 | test accuracy: 47.510 | epoch runtime:   4.28 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:12:46,754 - INFO - Evaluate Summary Time 1.81s\tLoss 1.6922\t Acc@1 46.4700\t Acc@5 88.2800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:12:46,754 - INFO - Head 82.767\tMid 42.900\tTail 14.933\u001b[0m\n",
      "\u001b[32m2024-10-06 15:12:46,754 - INFO - epoch: 130 | train loss: 2.3926 | train accuracy: 99.847 | test loss: 1.6922 | test accuracy: 46.470 | epoch runtime:   4.29 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:12:50,870 - INFO - Evaluate Summary Time 1.64s\tLoss 1.6899\t Acc@1 47.0600\t Acc@5 88.1200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:12:50,871 - INFO - Head 83.900\tMid 42.175\tTail 16.733\u001b[0m\n",
      "\u001b[32m2024-10-06 15:12:50,871 - INFO - epoch: 131 | train loss: 2.3884 | train accuracy: 99.863 | test loss: 1.6899 | test accuracy: 47.060 | epoch runtime:   4.12 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:12:54,996 - INFO - Evaluate Summary Time 1.69s\tLoss 1.6820\t Acc@1 47.8500\t Acc@5 88.0700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:12:54,996 - INFO - Head 84.200\tMid 44.575\tTail 15.867\u001b[0m\n",
      "\u001b[32m2024-10-06 15:12:54,997 - INFO - epoch: 132 | train loss: 2.3912 | train accuracy: 99.839 | test loss: 1.6820 | test accuracy: 47.850 | epoch runtime:   4.13 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:12:59,044 - INFO - Evaluate Summary Time 1.69s\tLoss 1.6829\t Acc@1 47.2000\t Acc@5 88.4000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:12:59,045 - INFO - Head 83.267\tMid 43.875\tTail 15.567\u001b[0m\n",
      "\u001b[32m2024-10-06 15:12:59,045 - INFO - epoch: 133 | train loss: 2.3897 | train accuracy: 99.879 | test loss: 1.6829 | test accuracy: 47.200 | epoch runtime:   4.05 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:13:03,158 - INFO - Evaluate Summary Time 1.71s\tLoss 1.6791\t Acc@1 47.5300\t Acc@5 88.6700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:13:03,159 - INFO - Head 83.967\tMid 43.825\tTail 16.033\u001b[0m\n",
      "\u001b[32m2024-10-06 15:13:03,159 - INFO - epoch: 134 | train loss: 2.3895 | train accuracy: 99.887 | test loss: 1.6791 | test accuracy: 47.530 | epoch runtime:   4.11 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:13:07,187 - INFO - Evaluate Summary Time 1.67s\tLoss 1.6894\t Acc@1 47.3700\t Acc@5 87.8200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:13:07,187 - INFO - Head 83.933\tMid 44.250\tTail 14.967\u001b[0m\n",
      "\u001b[32m2024-10-06 15:13:07,187 - INFO - epoch: 135 | train loss: 2.3910 | train accuracy: 99.887 | test loss: 1.6894 | test accuracy: 47.370 | epoch runtime:   4.03 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:13:11,312 - INFO - Evaluate Summary Time 1.74s\tLoss 1.6944\t Acc@1 47.0700\t Acc@5 87.9700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:13:11,313 - INFO - Head 83.733\tMid 43.450\tTail 15.233\u001b[0m\n",
      "\u001b[32m2024-10-06 15:13:11,313 - INFO - epoch: 136 | train loss: 2.3911 | train accuracy: 99.895 | test loss: 1.6944 | test accuracy: 47.070 | epoch runtime:   4.13 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:13:15,497 - INFO - Evaluate Summary Time 1.68s\tLoss 1.7019\t Acc@1 46.5800\t Acc@5 87.8100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:13:15,497 - INFO - Head 82.967\tMid 43.850\tTail 13.833\u001b[0m\n",
      "\u001b[32m2024-10-06 15:13:15,498 - INFO - epoch: 137 | train loss: 2.3886 | train accuracy: 99.879 | test loss: 1.7019 | test accuracy: 46.580 | epoch runtime:   4.18 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:13:19,645 - INFO - Evaluate Summary Time 1.72s\tLoss 1.6854\t Acc@1 47.2200\t Acc@5 88.2200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:13:19,646 - INFO - Head 84.000\tMid 42.525\tTail 16.700\u001b[0m\n",
      "\u001b[32m2024-10-06 15:13:19,646 - INFO - epoch: 138 | train loss: 2.3882 | train accuracy: 99.879 | test loss: 1.6854 | test accuracy: 47.220 | epoch runtime:   4.15 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:13:23,837 - INFO - Evaluate Summary Time 1.78s\tLoss 1.6969\t Acc@1 46.7100\t Acc@5 88.2800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:13:23,837 - INFO - Head 83.233\tMid 43.475\tTail 14.500\u001b[0m\n",
      "\u001b[32m2024-10-06 15:13:23,838 - INFO - epoch: 139 | train loss: 2.3898 | train accuracy: 99.887 | test loss: 1.6969 | test accuracy: 46.710 | epoch runtime:   4.19 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:13:27,927 - INFO - Evaluate Summary Time 1.65s\tLoss 1.6916\t Acc@1 47.0500\t Acc@5 88.2600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:13:27,928 - INFO - Head 83.733\tMid 43.275\tTail 15.400\u001b[0m\n",
      "\u001b[32m2024-10-06 15:13:27,928 - INFO - epoch: 140 | train loss: 2.3898 | train accuracy: 99.847 | test loss: 1.6916 | test accuracy: 47.050 | epoch runtime:   4.09 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:13:32,055 - INFO - Evaluate Summary Time 1.74s\tLoss 1.6868\t Acc@1 47.0300\t Acc@5 88.5400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:13:32,056 - INFO - Head 82.667\tMid 43.900\tTail 15.567\u001b[0m\n",
      "\u001b[32m2024-10-06 15:13:32,056 - INFO - epoch: 141 | train loss: 2.3907 | train accuracy: 99.871 | test loss: 1.6868 | test accuracy: 47.030 | epoch runtime:   4.13 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:13:36,276 - INFO - Evaluate Summary Time 1.78s\tLoss 1.6850\t Acc@1 47.1800\t Acc@5 88.4200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:13:36,277 - INFO - Head 82.433\tMid 43.750\tTail 16.500\u001b[0m\n",
      "\u001b[32m2024-10-06 15:13:36,277 - INFO - epoch: 142 | train loss: 2.3898 | train accuracy: 99.855 | test loss: 1.6850 | test accuracy: 47.180 | epoch runtime:   4.22 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:13:40,620 - INFO - Evaluate Summary Time 1.83s\tLoss 1.6981\t Acc@1 46.8100\t Acc@5 88.0200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:13:40,621 - INFO - Head 82.700\tMid 43.650\tTail 15.133\u001b[0m\n",
      "\u001b[32m2024-10-06 15:13:40,621 - INFO - epoch: 143 | train loss: 2.3911 | train accuracy: 99.871 | test loss: 1.6981 | test accuracy: 46.810 | epoch runtime:   4.34 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:13:44,879 - INFO - Evaluate Summary Time 1.78s\tLoss 1.6962\t Acc@1 46.4700\t Acc@5 88.4900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:13:44,879 - INFO - Head 82.900\tMid 43.125\tTail 14.500\u001b[0m\n",
      "\u001b[32m2024-10-06 15:13:44,879 - INFO - epoch: 144 | train loss: 2.3892 | train accuracy: 99.879 | test loss: 1.6962 | test accuracy: 46.470 | epoch runtime:   4.26 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:13:49,024 - INFO - Evaluate Summary Time 1.70s\tLoss 1.7110\t Acc@1 45.9000\t Acc@5 87.9600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:13:49,025 - INFO - Head 82.233\tMid 41.550\tTail 15.367\u001b[0m\n",
      "\u001b[32m2024-10-06 15:13:49,025 - INFO - epoch: 145 | train loss: 2.3891 | train accuracy: 99.855 | test loss: 1.7110 | test accuracy: 45.900 | epoch runtime:   4.15 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:13:53,241 - INFO - Evaluate Summary Time 1.78s\tLoss 1.6699\t Acc@1 48.1800\t Acc@5 88.1000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:13:53,242 - INFO - Head 84.500\tMid 45.150\tTail 15.900\u001b[0m\n",
      "\u001b[32m2024-10-06 15:13:53,242 - INFO - epoch: 146 | train loss: 2.3887 | train accuracy: 99.911 | test loss: 1.6699 | test accuracy: 48.180 | epoch runtime:   4.22 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:13:57,368 - INFO - Evaluate Summary Time 1.68s\tLoss 1.6871\t Acc@1 47.1100\t Acc@5 88.2500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:13:57,368 - INFO - Head 83.333\tMid 43.050\tTail 16.300\u001b[0m\n",
      "\u001b[32m2024-10-06 15:13:57,368 - INFO - epoch: 147 | train loss: 2.3891 | train accuracy: 99.903 | test loss: 1.6871 | test accuracy: 47.110 | epoch runtime:   4.13 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:14:01,543 - INFO - Evaluate Summary Time 1.74s\tLoss 1.6830\t Acc@1 47.0000\t Acc@5 88.6000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:14:01,543 - INFO - Head 83.433\tMid 43.225\tTail 15.600\u001b[0m\n",
      "\u001b[32m2024-10-06 15:14:01,544 - INFO - epoch: 148 | train loss: 2.3892 | train accuracy: 99.903 | test loss: 1.6830 | test accuracy: 47.000 | epoch runtime:   4.18 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:14:05,803 - INFO - Evaluate Summary Time 1.79s\tLoss 1.6865\t Acc@1 47.3300\t Acc@5 88.4700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:14:05,803 - INFO - Head 83.800\tMid 43.800\tTail 15.567\u001b[0m\n",
      "\u001b[32m2024-10-06 15:14:05,804 - INFO - epoch: 149 | train loss: 2.3886 | train accuracy: 99.847 | test loss: 1.6865 | test accuracy: 47.330 | epoch runtime:   4.26 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "\u001b[32m2024-10-06 15:14:09,947 - INFO - Evaluate Summary Time 1.71s\tLoss 1.6876\t Acc@1 47.2900\t Acc@5 88.2300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:14:09,947 - INFO - Head 83.133\tMid 44.150\tTail 15.633\u001b[0m\n",
      "\u001b[32m2024-10-06 15:14:09,947 - INFO - epoch: 150 | train loss: 2.3901 | train accuracy: 99.879 | test loss: 1.6876 | test accuracy: 47.290 | epoch runtime:   4.14 sec | best accuracy: 48.580 @ epoch: 085\u001b[0m\n",
      "Runtime of this script /home/zyx/zhengjinpeng/PNP/cifar.py : 628.0 seconds (0.174 hours)\n"
     ]
    }
   ],
   "source": [
    "# !python cifar.py\n",
    "# !python cifar.py --r_ood 0.2\n",
    "!python cifar.py --r_ood 0.2 --closeset-ratio 0.2\n",
    "!python cifar.py --r_ood 0.2 --closeset-ratio 0.2 --noise-type asymmetric\n",
    "# !python cifar.py --r_imb 0.01\n",
    "# !python cifar.py --r_imb 0.01 --r_ood 0.2\n",
    "!python cifar.py --r_imb 0.01 --r_ood 0.2 --closeset-ratio 0.2\n",
    "!python cifar.py --r_imb 0.01 --r_ood 0.2 --closeset-ratio 0.2 --noise-type asymmetric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "17287af3e8d15e58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Config:\n",
      "{\n",
      "    database: Datasets\n",
      "    dataset: cifar100\n",
      "    n_classes: 100\n",
      "    rescale_size: 32\n",
      "    crop_size: 32\n",
      "    cfg_file: ./config/cifar100.cfg\n",
      "    synthetic_data: cifar80no\n",
      "    noise_type: symmetric\n",
      "    closeset_ratio: 0.2\n",
      "    r_ood: 0.2\n",
      "    r_imb: 0.1\n",
      "    gpu: 0\n",
      "    net: cnn\n",
      "    batch_size: 128\n",
      "    lr: 0.001\n",
      "    lr_decay: cosine\n",
      "    weight_decay: 1e-05\n",
      "    opt: adam\n",
      "    warmup_epochs: 5\n",
      "    warmup_lr_scale: 10.0\n",
      "    epochs: 150\n",
      "    save_model: False\n",
      "    use_fp16: False\n",
      "    use_grad_accumulate: False\n",
      "    project: \n",
      "    log: PENIOC\n",
      "    epsilon: 0.5\n",
      "    temperature: 0.1\n",
      "    eta: 0.5\n",
      "    alpha: 0.0\n",
      "    beta: 1.0\n",
      "    gamma: 1.0\n",
      "    omega: 0.1\n",
      "    rho: 1.0\n",
      "    loss_func_aux: mae\n",
      "    weighting: soft\n",
      "    neg_cons: False\n",
      "    activation: tanh\n",
      "    ablation: False\n",
      "    log_freq: 1\n",
      "    asym: False\n",
      "}\n",
      "\n",
      "Available GPUs Index : 0\n",
      "using CIFAR-100...\n",
      "Built imbalanced dataset, r_imb=0.1\n",
      "Mixing in OOD noise, r_ood=0.2\n",
      "Mixing in ID noise, r_id=0.2\n",
      "using CIFAR-100...\n",
      "\u001b[32m2024-10-06 15:14:17,314 - INFO - Categories: 100, Training Samples: 19573, Testing Samples: 10000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:14:17,314 - INFO - Optimizer: adam\u001b[0m\n",
      "\u001b[32m2024-10-06 15:14:17,314 - INFO - Accumulate gradients every 1 iterations --> Acutal batch size is 128\u001b[0m\n",
      "\u001b[32m2024-10-06 15:14:23,017 - INFO - Evaluate Summary Time 1.72s\tLoss 4.3369\t Acc@1 6.5600\t Acc@5 22.2500\u001b[0m                                        \n",
      "\u001b[32m2024-10-06 15:14:23,017 - INFO - Head 15.944\tMid 2.343\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:14:23,017 - INFO - epoch:   1 | train loss: 4.6756 | train accuracy:  4.920 | test loss: 4.3369 | test accuracy:  6.560 | epoch runtime:   5.70 sec | best accuracy:  6.560 @ epoch: 001\u001b[0m\n",
      "\u001b[32m2024-10-06 15:14:28,002 - INFO - Evaluate Summary Time 1.74s\tLoss 4.1947\t Acc@1 9.2700\t Acc@5 27.0500\u001b[0m                                        \n",
      "\u001b[32m2024-10-06 15:14:28,003 - INFO - Head 20.000\tMid 5.800\tTail 0.138\u001b[0m\n",
      "\u001b[32m2024-10-06 15:14:28,003 - INFO - epoch:   2 | train loss: 4.5062 | train accuracy:  9.166 | test loss: 4.1947 | test accuracy:  9.270 | epoch runtime:   4.99 sec | best accuracy:  9.270 @ epoch: 002\u001b[0m\n",
      "\u001b[32m2024-10-06 15:14:32,737 - INFO - Evaluate Summary Time 1.62s\tLoss 4.0041\t Acc@1 13.0800\t Acc@5 33.5100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:14:32,737 - INFO - Head 27.500\tMid 8.771\tTail 0.379\u001b[0m\n",
      "\u001b[32m2024-10-06 15:14:32,737 - INFO - epoch:   3 | train loss: 4.4592 | train accuracy: 11.480 | test loss: 4.0041 | test accuracy: 13.080 | epoch runtime:   4.73 sec | best accuracy: 13.080 @ epoch: 003\u001b[0m\n",
      "\u001b[32m2024-10-06 15:14:37,793 - INFO - Evaluate Summary Time 1.80s\tLoss 3.9753\t Acc@1 15.0500\t Acc@5 37.4000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:14:37,793 - INFO - Head 28.361\tMid 11.600\tTail 2.690\u001b[0m\n",
      "\u001b[32m2024-10-06 15:14:37,793 - INFO - epoch:   4 | train loss: 4.4220 | train accuracy: 14.280 | test loss: 3.9753 | test accuracy: 15.050 | epoch runtime:   5.06 sec | best accuracy: 15.050 @ epoch: 004\u001b[0m\n",
      "\u001b[32m2024-10-06 15:14:42,932 - INFO - Evaluate Summary Time 1.84s\tLoss 3.8560\t Acc@1 17.2400\t Acc@5 39.7100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:14:42,933 - INFO - Head 33.000\tMid 13.314\tTail 2.414\u001b[0m\n",
      "\u001b[32m2024-10-06 15:14:42,933 - INFO - epoch:   5 | train loss: 4.3870 | train accuracy: 16.773 | test loss: 3.8560 | test accuracy: 17.240 | epoch runtime:   5.14 sec | best accuracy: 17.240 @ epoch: 005\u001b[0m\n",
      "\u001b[32m2024-10-06 15:14:48,486 - INFO - Evaluate Summary Time 1.75s\tLoss 3.9429\t Acc@1 19.5800\t Acc@5 43.6500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:14:48,486 - INFO - Head 35.722\tMid 17.000\tTail 2.655\u001b[0m\n",
      "\u001b[32m2024-10-06 15:14:48,486 - INFO - epoch:   6 | train loss: 4.9664 | train accuracy: 19.670 | test loss: 3.9429 | test accuracy: 19.580 | epoch runtime:   5.55 sec | best accuracy: 19.580 @ epoch: 006\u001b[0m\n",
      "\u001b[32m2024-10-06 15:14:53,830 - INFO - Evaluate Summary Time 1.72s\tLoss 3.9056\t Acc@1 20.1500\t Acc@5 45.2100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:14:53,830 - INFO - Head 35.972\tMid 18.257\tTail 2.793\u001b[0m\n",
      "\u001b[32m2024-10-06 15:14:53,830 - INFO - epoch:   7 | train loss: 4.9159 | train accuracy: 21.177 | test loss: 3.9056 | test accuracy: 20.150 | epoch runtime:   5.34 sec | best accuracy: 20.150 @ epoch: 007\u001b[0m\n",
      "\u001b[32m2024-10-06 15:14:59,264 - INFO - Evaluate Summary Time 1.70s\tLoss 3.8982\t Acc@1 20.5300\t Acc@5 45.6800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:14:59,264 - INFO - Head 36.583\tMid 18.543\tTail 3.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:14:59,265 - INFO - epoch:   8 | train loss: 4.8993 | train accuracy: 21.867 | test loss: 3.8982 | test accuracy: 20.530 | epoch runtime:   5.43 sec | best accuracy: 20.530 @ epoch: 008\u001b[0m\n",
      "\u001b[32m2024-10-06 15:15:04,658 - INFO - Evaluate Summary Time 1.76s\tLoss 3.8671\t Acc@1 20.9500\t Acc@5 45.8300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:15:04,658 - INFO - Head 38.056\tMid 18.514\tTail 2.655\u001b[0m\n",
      "\u001b[32m2024-10-06 15:15:04,658 - INFO - epoch:   9 | train loss: 4.8816 | train accuracy: 22.179 | test loss: 3.8671 | test accuracy: 20.950 | epoch runtime:   5.39 sec | best accuracy: 20.950 @ epoch: 009\u001b[0m\n",
      "\u001b[32m2024-10-06 15:15:10,022 - INFO - Evaluate Summary Time 1.65s\tLoss 3.8691\t Acc@1 21.3200\t Acc@5 46.1200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:15:10,022 - INFO - Head 37.889\tMid 19.514\tTail 2.931\u001b[0m\n",
      "\u001b[32m2024-10-06 15:15:10,023 - INFO - epoch:  10 | train loss: 4.8594 | train accuracy: 22.664 | test loss: 3.8691 | test accuracy: 21.320 | epoch runtime:   5.36 sec | best accuracy: 21.320 @ epoch: 010\u001b[0m\n",
      "\u001b[32m2024-10-06 15:15:15,324 - INFO - Evaluate Summary Time 1.70s\tLoss 3.8765\t Acc@1 21.5700\t Acc@5 46.5600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:15:15,324 - INFO - Head 37.556\tMid 20.029\tTail 3.586\u001b[0m\n",
      "\u001b[32m2024-10-06 15:15:15,324 - INFO - epoch:  11 | train loss: 4.8362 | train accuracy: 23.180 | test loss: 3.8765 | test accuracy: 21.570 | epoch runtime:   5.30 sec | best accuracy: 21.570 @ epoch: 011\u001b[0m\n",
      "\u001b[32m2024-10-06 15:15:20,815 - INFO - Evaluate Summary Time 1.76s\tLoss 3.9674\t Acc@1 21.4500\t Acc@5 46.3400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:15:20,816 - INFO - Head 38.194\tMid 19.457\tTail 3.069\u001b[0m\n",
      "\u001b[32m2024-10-06 15:15:20,816 - INFO - epoch:  12 | train loss: 4.8194 | train accuracy: 24.273 | test loss: 3.9674 | test accuracy: 21.450 | epoch runtime:   5.49 sec | best accuracy: 21.570 @ epoch: 011\u001b[0m\n",
      "\u001b[32m2024-10-06 15:15:26,204 - INFO - Evaluate Summary Time 1.77s\tLoss 4.0019\t Acc@1 21.5400\t Acc@5 46.5000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:15:26,204 - INFO - Head 37.528\tMid 19.686\tTail 3.931\u001b[0m\n",
      "\u001b[32m2024-10-06 15:15:26,204 - INFO - epoch:  13 | train loss: 4.8061 | train accuracy: 25.116 | test loss: 4.0019 | test accuracy: 21.540 | epoch runtime:   5.39 sec | best accuracy: 21.570 @ epoch: 011\u001b[0m\n",
      "\u001b[32m2024-10-06 15:15:31,587 - INFO - Evaluate Summary Time 1.71s\tLoss 3.9719\t Acc@1 22.0100\t Acc@5 47.7300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:15:31,587 - INFO - Head 38.500\tMid 20.114\tTail 3.828\u001b[0m\n",
      "\u001b[32m2024-10-06 15:15:31,587 - INFO - epoch:  14 | train loss: 4.7955 | train accuracy: 25.745 | test loss: 3.9719 | test accuracy: 22.010 | epoch runtime:   5.38 sec | best accuracy: 22.010 @ epoch: 014\u001b[0m\n",
      "\u001b[32m2024-10-06 15:15:37,018 - INFO - Evaluate Summary Time 1.71s\tLoss 3.9936\t Acc@1 22.5700\t Acc@5 48.0400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:15:37,019 - INFO - Head 38.583\tMid 20.857\tTail 4.759\u001b[0m\n",
      "\u001b[32m2024-10-06 15:15:37,019 - INFO - epoch:  15 | train loss: 4.7917 | train accuracy: 26.659 | test loss: 3.9936 | test accuracy: 22.570 | epoch runtime:   5.43 sec | best accuracy: 22.570 @ epoch: 015\u001b[0m\n",
      "\u001b[32m2024-10-06 15:15:42,389 - INFO - Evaluate Summary Time 1.77s\tLoss 3.9947\t Acc@1 22.4800\t Acc@5 48.2000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:15:42,389 - INFO - Head 39.194\tMid 20.314\tTail 4.345\u001b[0m\n",
      "\u001b[32m2024-10-06 15:15:42,389 - INFO - epoch:  16 | train loss: 4.7786 | train accuracy: 27.385 | test loss: 3.9947 | test accuracy: 22.480 | epoch runtime:   5.37 sec | best accuracy: 22.570 @ epoch: 015\u001b[0m\n",
      "\u001b[32m2024-10-06 15:15:47,873 - INFO - Evaluate Summary Time 1.70s\tLoss 3.9381\t Acc@1 23.8600\t Acc@5 49.7300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:15:47,874 - INFO - Head 39.444\tMid 22.943\tTail 5.621\u001b[0m\n",
      "\u001b[32m2024-10-06 15:15:47,874 - INFO - epoch:  17 | train loss: 4.7717 | train accuracy: 28.028 | test loss: 3.9381 | test accuracy: 23.860 | epoch runtime:   5.48 sec | best accuracy: 23.860 @ epoch: 017\u001b[0m\n",
      "\u001b[32m2024-10-06 15:15:53,257 - INFO - Evaluate Summary Time 1.74s\tLoss 3.9746\t Acc@1 23.2800\t Acc@5 49.2700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:15:53,257 - INFO - Head 39.556\tMid 21.943\tTail 4.690\u001b[0m\n",
      "\u001b[32m2024-10-06 15:15:53,257 - INFO - epoch:  18 | train loss: 4.7622 | train accuracy: 29.193 | test loss: 3.9746 | test accuracy: 23.280 | epoch runtime:   5.38 sec | best accuracy: 23.860 @ epoch: 017\u001b[0m\n",
      "\u001b[32m2024-10-06 15:15:58,559 - INFO - Evaluate Summary Time 1.64s\tLoss 3.9814\t Acc@1 23.2800\t Acc@5 49.5300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:15:58,559 - INFO - Head 39.028\tMid 21.800\tTail 5.517\u001b[0m\n",
      "\u001b[32m2024-10-06 15:15:58,559 - INFO - epoch:  19 | train loss: 4.7560 | train accuracy: 29.387 | test loss: 3.9814 | test accuracy: 23.280 | epoch runtime:   5.30 sec | best accuracy: 23.860 @ epoch: 017\u001b[0m\n",
      "\u001b[32m2024-10-06 15:16:03,899 - INFO - Evaluate Summary Time 1.70s\tLoss 3.9498\t Acc@1 23.8300\t Acc@5 49.7700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:16:03,900 - INFO - Head 39.500\tMid 22.886\tTail 5.517\u001b[0m\n",
      "\u001b[32m2024-10-06 15:16:03,900 - INFO - epoch:  20 | train loss: 4.7459 | train accuracy: 30.603 | test loss: 3.9498 | test accuracy: 23.830 | epoch runtime:   5.34 sec | best accuracy: 23.860 @ epoch: 017\u001b[0m\n",
      "\u001b[32m2024-10-06 15:16:09,405 - INFO - Evaluate Summary Time 1.78s\tLoss 3.9406\t Acc@1 24.3900\t Acc@5 49.7200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:16:09,406 - INFO - Head 40.361\tMid 23.171\tTail 6.034\u001b[0m\n",
      "\u001b[32m2024-10-06 15:16:09,406 - INFO - epoch:  21 | train loss: 4.7384 | train accuracy: 31.671 | test loss: 3.9406 | test accuracy: 24.390 | epoch runtime:   5.51 sec | best accuracy: 24.390 @ epoch: 021\u001b[0m\n",
      "\u001b[32m2024-10-06 15:16:14,743 - INFO - Evaluate Summary Time 1.75s\tLoss 3.8316\t Acc@1 24.8500\t Acc@5 51.2500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:16:14,743 - INFO - Head 40.861\tMid 24.114\tTail 5.862\u001b[0m\n",
      "\u001b[32m2024-10-06 15:16:14,744 - INFO - epoch:  22 | train loss: 4.7310 | train accuracy: 32.417 | test loss: 3.8316 | test accuracy: 24.850 | epoch runtime:   5.34 sec | best accuracy: 24.850 @ epoch: 022\u001b[0m\n",
      "\u001b[32m2024-10-06 15:16:20,244 - INFO - Evaluate Summary Time 1.76s\tLoss 3.9176\t Acc@1 24.6700\t Acc@5 50.2100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:16:20,245 - INFO - Head 40.333\tMid 24.029\tTail 6.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:16:20,245 - INFO - epoch:  23 | train loss: 4.7221 | train accuracy: 33.214 | test loss: 3.9176 | test accuracy: 24.670 | epoch runtime:   5.50 sec | best accuracy: 24.850 @ epoch: 022\u001b[0m\n",
      "\u001b[32m2024-10-06 15:16:25,764 - INFO - Evaluate Summary Time 1.84s\tLoss 3.8847\t Acc@1 25.1400\t Acc@5 50.6400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:16:25,764 - INFO - Head 41.222\tMid 24.314\tTail 6.172\u001b[0m\n",
      "\u001b[32m2024-10-06 15:16:25,765 - INFO - epoch:  24 | train loss: 4.7134 | train accuracy: 34.696 | test loss: 3.8847 | test accuracy: 25.140 | epoch runtime:   5.52 sec | best accuracy: 25.140 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 15:16:31,160 - INFO - Evaluate Summary Time 1.72s\tLoss 3.8929\t Acc@1 25.4200\t Acc@5 50.3800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:16:31,161 - INFO - Head 42.222\tMid 23.486\tTail 6.897\u001b[0m\n",
      "\u001b[32m2024-10-06 15:16:31,161 - INFO - epoch:  25 | train loss: 4.7055 | train accuracy: 34.987 | test loss: 3.8929 | test accuracy: 25.420 | epoch runtime:   5.40 sec | best accuracy: 25.420 @ epoch: 025\u001b[0m\n",
      "\u001b[32m2024-10-06 15:16:36,585 - INFO - Evaluate Summary Time 1.73s\tLoss 3.8638\t Acc@1 25.5000\t Acc@5 51.1200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:16:36,586 - INFO - Head 41.694\tMid 24.629\tTail 6.448\u001b[0m\n",
      "\u001b[32m2024-10-06 15:16:36,586 - INFO - epoch:  26 | train loss: 4.6974 | train accuracy: 36.474 | test loss: 3.8638 | test accuracy: 25.500 | epoch runtime:   5.43 sec | best accuracy: 25.500 @ epoch: 026\u001b[0m\n",
      "\u001b[32m2024-10-06 15:16:41,996 - INFO - Evaluate Summary Time 1.77s\tLoss 3.8739\t Acc@1 25.5400\t Acc@5 50.7900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:16:41,996 - INFO - Head 41.861\tMid 24.600\tTail 6.414\u001b[0m\n",
      "\u001b[32m2024-10-06 15:16:41,997 - INFO - epoch:  27 | train loss: 4.7205 | train accuracy: 36.561 | test loss: 3.8739 | test accuracy: 25.540 | epoch runtime:   5.41 sec | best accuracy: 25.540 @ epoch: 027\u001b[0m\n",
      "\u001b[32m2024-10-06 15:16:47,536 - INFO - Evaluate Summary Time 1.79s\tLoss 3.8576\t Acc@1 26.1700\t Acc@5 51.5300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:16:47,537 - INFO - Head 42.306\tMid 25.314\tTail 7.172\u001b[0m\n",
      "\u001b[32m2024-10-06 15:16:47,537 - INFO - epoch:  28 | train loss: 4.6831 | train accuracy: 38.211 | test loss: 3.8576 | test accuracy: 26.170 | epoch runtime:   5.54 sec | best accuracy: 26.170 @ epoch: 028\u001b[0m\n",
      "\u001b[32m2024-10-06 15:16:53,023 - INFO - Evaluate Summary Time 1.81s\tLoss 3.8029\t Acc@1 26.9300\t Acc@5 52.1300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:16:53,023 - INFO - Head 42.944\tMid 26.086\tTail 8.069\u001b[0m\n",
      "\u001b[32m2024-10-06 15:16:53,023 - INFO - epoch:  29 | train loss: 4.6715 | train accuracy: 39.371 | test loss: 3.8029 | test accuracy: 26.930 | epoch runtime:   5.49 sec | best accuracy: 26.930 @ epoch: 029\u001b[0m\n",
      "\u001b[32m2024-10-06 15:16:58,448 - INFO - Evaluate Summary Time 1.75s\tLoss 3.8194\t Acc@1 26.3500\t Acc@5 52.0800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:16:58,449 - INFO - Head 42.917\tMid 25.343\tTail 7.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:16:58,449 - INFO - epoch:  30 | train loss: 4.6625 | train accuracy: 40.919 | test loss: 3.8194 | test accuracy: 26.350 | epoch runtime:   5.43 sec | best accuracy: 26.930 @ epoch: 029\u001b[0m\n",
      "\u001b[32m2024-10-06 15:17:03,908 - INFO - Evaluate Summary Time 1.77s\tLoss 3.8154\t Acc@1 26.3300\t Acc@5 51.6100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:17:03,908 - INFO - Head 42.278\tMid 25.686\tTail 7.310\u001b[0m\n",
      "\u001b[32m2024-10-06 15:17:03,908 - INFO - epoch:  31 | train loss: 4.6531 | train accuracy: 41.981 | test loss: 3.8154 | test accuracy: 26.330 | epoch runtime:   5.46 sec | best accuracy: 26.930 @ epoch: 029\u001b[0m\n",
      "\u001b[32m2024-10-06 15:17:09,375 - INFO - Evaluate Summary Time 1.74s\tLoss 3.7605\t Acc@1 26.9100\t Acc@5 52.4900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:17:09,375 - INFO - Head 43.222\tMid 26.171\tTail 7.552\u001b[0m\n",
      "\u001b[32m2024-10-06 15:17:09,375 - INFO - epoch:  32 | train loss: 4.6441 | train accuracy: 43.397 | test loss: 3.7605 | test accuracy: 26.910 | epoch runtime:   5.47 sec | best accuracy: 26.930 @ epoch: 029\u001b[0m\n",
      "\u001b[32m2024-10-06 15:17:14,887 - INFO - Evaluate Summary Time 1.82s\tLoss 3.7963\t Acc@1 26.9400\t Acc@5 52.2200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:17:14,888 - INFO - Head 42.528\tMid 26.743\tTail 7.828\u001b[0m\n",
      "\u001b[32m2024-10-06 15:17:14,888 - INFO - epoch:  33 | train loss: 4.6344 | train accuracy: 44.817 | test loss: 3.7963 | test accuracy: 26.940 | epoch runtime:   5.51 sec | best accuracy: 26.940 @ epoch: 033\u001b[0m\n",
      "\u001b[32m2024-10-06 15:17:20,319 - INFO - Evaluate Summary Time 1.69s\tLoss 3.7331\t Acc@1 27.3600\t Acc@5 52.3100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:17:20,319 - INFO - Head 43.500\tMid 27.057\tTail 7.690\u001b[0m\n",
      "\u001b[32m2024-10-06 15:17:20,320 - INFO - epoch:  34 | train loss: 4.6258 | train accuracy: 46.411 | test loss: 3.7331 | test accuracy: 27.360 | epoch runtime:   5.43 sec | best accuracy: 27.360 @ epoch: 034\u001b[0m\n",
      "\u001b[32m2024-10-06 15:17:25,757 - INFO - Evaluate Summary Time 1.78s\tLoss 3.7173\t Acc@1 27.5200\t Acc@5 52.5200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:17:25,757 - INFO - Head 43.833\tMid 26.943\tTail 7.966\u001b[0m\n",
      "\u001b[32m2024-10-06 15:17:25,757 - INFO - epoch:  35 | train loss: 4.6151 | train accuracy: 47.361 | test loss: 3.7173 | test accuracy: 27.520 | epoch runtime:   5.44 sec | best accuracy: 27.520 @ epoch: 035\u001b[0m\n",
      "\u001b[32m2024-10-06 15:17:31,027 - INFO - Evaluate Summary Time 1.68s\tLoss 3.6848\t Acc@1 28.0500\t Acc@5 52.7400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:17:31,028 - INFO - Head 43.722\tMid 28.543\tTail 8.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:17:31,028 - INFO - epoch:  36 | train loss: 4.6070 | train accuracy: 48.935 | test loss: 3.6848 | test accuracy: 28.050 | epoch runtime:   5.27 sec | best accuracy: 28.050 @ epoch: 036\u001b[0m\n",
      "\u001b[32m2024-10-06 15:17:36,536 - INFO - Evaluate Summary Time 1.77s\tLoss 3.7022\t Acc@1 28.1800\t Acc@5 52.4200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:17:36,537 - INFO - Head 43.778\tMid 28.286\tTail 8.690\u001b[0m\n",
      "\u001b[32m2024-10-06 15:17:36,537 - INFO - epoch:  37 | train loss: 4.5967 | train accuracy: 50.442 | test loss: 3.7022 | test accuracy: 28.180 | epoch runtime:   5.51 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:17:41,973 - INFO - Evaluate Summary Time 1.80s\tLoss 3.7517\t Acc@1 27.4000\t Acc@5 51.6500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:17:41,973 - INFO - Head 43.556\tMid 27.257\tTail 7.517\u001b[0m\n",
      "\u001b[32m2024-10-06 15:17:41,973 - INFO - epoch:  38 | train loss: 4.5888 | train accuracy: 52.056 | test loss: 3.7517 | test accuracy: 27.400 | epoch runtime:   5.44 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:17:47,433 - INFO - Evaluate Summary Time 1.66s\tLoss 3.7231\t Acc@1 27.6600\t Acc@5 52.2400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:17:47,434 - INFO - Head 44.056\tMid 27.057\tTail 8.034\u001b[0m\n",
      "\u001b[32m2024-10-06 15:17:47,434 - INFO - epoch:  39 | train loss: 4.5794 | train accuracy: 53.405 | test loss: 3.7231 | test accuracy: 27.660 | epoch runtime:   5.46 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:17:52,891 - INFO - Evaluate Summary Time 1.79s\tLoss 3.7277\t Acc@1 27.7000\t Acc@5 51.2700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:17:52,892 - INFO - Head 44.389\tMid 27.229\tTail 7.552\u001b[0m\n",
      "\u001b[32m2024-10-06 15:17:52,892 - INFO - epoch:  40 | train loss: 4.5695 | train accuracy: 55.178 | test loss: 3.7277 | test accuracy: 27.700 | epoch runtime:   5.46 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:17:58,300 - INFO - Evaluate Summary Time 1.69s\tLoss 3.7521\t Acc@1 27.3400\t Acc@5 50.3500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:17:58,301 - INFO - Head 43.889\tMid 26.400\tTail 7.931\u001b[0m\n",
      "\u001b[32m2024-10-06 15:17:58,301 - INFO - epoch:  41 | train loss: 4.5598 | train accuracy: 56.614 | test loss: 3.7521 | test accuracy: 27.340 | epoch runtime:   5.41 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:18:03,808 - INFO - Evaluate Summary Time 1.79s\tLoss 3.7022\t Acc@1 27.9600\t Acc@5 51.4100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:18:03,808 - INFO - Head 44.111\tMid 27.029\tTail 9.034\u001b[0m\n",
      "\u001b[32m2024-10-06 15:18:03,808 - INFO - epoch:  42 | train loss: 4.5558 | train accuracy: 58.504 | test loss: 3.7022 | test accuracy: 27.960 | epoch runtime:   5.51 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:18:09,259 - INFO - Evaluate Summary Time 1.68s\tLoss 3.6922\t Acc@1 27.6300\t Acc@5 51.6200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:18:09,259 - INFO - Head 43.083\tMid 28.171\tTail 7.793\u001b[0m\n",
      "\u001b[32m2024-10-06 15:18:09,259 - INFO - epoch:  43 | train loss: 4.5417 | train accuracy: 60.185 | test loss: 3.6922 | test accuracy: 27.630 | epoch runtime:   5.45 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:18:14,744 - INFO - Evaluate Summary Time 1.76s\tLoss 3.7074\t Acc@1 27.3200\t Acc@5 50.7800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:18:14,745 - INFO - Head 43.917\tMid 27.000\tTail 7.103\u001b[0m\n",
      "\u001b[32m2024-10-06 15:18:14,745 - INFO - epoch:  44 | train loss: 4.5339 | train accuracy: 61.585 | test loss: 3.7074 | test accuracy: 27.320 | epoch runtime:   5.49 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:18:20,282 - INFO - Evaluate Summary Time 1.80s\tLoss 3.7008\t Acc@1 27.6300\t Acc@5 50.9800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:18:20,283 - INFO - Head 44.056\tMid 27.400\tTail 7.517\u001b[0m\n",
      "\u001b[32m2024-10-06 15:18:20,283 - INFO - epoch:  45 | train loss: 4.5244 | train accuracy: 63.552 | test loss: 3.7008 | test accuracy: 27.630 | epoch runtime:   5.54 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:18:25,750 - INFO - Evaluate Summary Time 1.71s\tLoss 3.7165\t Acc@1 27.2800\t Acc@5 51.1300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:18:25,750 - INFO - Head 44.278\tMid 25.714\tTail 8.069\u001b[0m\n",
      "\u001b[32m2024-10-06 15:18:25,751 - INFO - epoch:  46 | train loss: 4.5174 | train accuracy: 65.100 | test loss: 3.7165 | test accuracy: 27.280 | epoch runtime:   5.47 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:18:31,115 - INFO - Evaluate Summary Time 1.75s\tLoss 3.7366\t Acc@1 27.2600\t Acc@5 50.6200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:18:31,116 - INFO - Head 43.500\tMid 26.629\tTail 7.862\u001b[0m\n",
      "\u001b[32m2024-10-06 15:18:31,116 - INFO - epoch:  47 | train loss: 4.5098 | train accuracy: 66.863 | test loss: 3.7366 | test accuracy: 27.260 | epoch runtime:   5.37 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:18:36,649 - INFO - Evaluate Summary Time 1.84s\tLoss 3.6765\t Acc@1 27.5600\t Acc@5 50.8700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:18:36,649 - INFO - Head 43.694\tMid 27.657\tTail 7.414\u001b[0m\n",
      "\u001b[32m2024-10-06 15:18:36,649 - INFO - epoch:  48 | train loss: 4.5004 | train accuracy: 68.329 | test loss: 3.6765 | test accuracy: 27.560 | epoch runtime:   5.53 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:18:41,930 - INFO - Evaluate Summary Time 1.69s\tLoss 3.6771\t Acc@1 26.9200\t Acc@5 50.5100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:18:41,930 - INFO - Head 43.111\tMid 26.457\tTail 7.379\u001b[0m\n",
      "\u001b[32m2024-10-06 15:18:41,930 - INFO - epoch:  49 | train loss: 4.4904 | train accuracy: 70.255 | test loss: 3.6771 | test accuracy: 26.920 | epoch runtime:   5.28 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:18:47,443 - INFO - Evaluate Summary Time 1.79s\tLoss 3.7039\t Acc@1 27.0300\t Acc@5 50.1400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:18:47,444 - INFO - Head 43.222\tMid 26.457\tTail 7.621\u001b[0m\n",
      "\u001b[32m2024-10-06 15:18:47,444 - INFO - epoch:  50 | train loss: 4.4843 | train accuracy: 71.742 | test loss: 3.7039 | test accuracy: 27.030 | epoch runtime:   5.51 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:18:52,860 - INFO - Evaluate Summary Time 1.69s\tLoss 3.6949\t Acc@1 27.4200\t Acc@5 50.6100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:18:52,860 - INFO - Head 43.028\tMid 27.429\tTail 8.034\u001b[0m\n",
      "\u001b[32m2024-10-06 15:18:52,861 - INFO - epoch:  51 | train loss: 4.4768 | train accuracy: 73.126 | test loss: 3.6949 | test accuracy: 27.420 | epoch runtime:   5.42 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:18:58,383 - INFO - Evaluate Summary Time 1.74s\tLoss 3.6787\t Acc@1 27.2500\t Acc@5 50.5600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:18:58,384 - INFO - Head 43.167\tMid 27.629\tTail 7.034\u001b[0m\n",
      "\u001b[32m2024-10-06 15:18:58,384 - INFO - epoch:  52 | train loss: 4.4687 | train accuracy: 74.797 | test loss: 3.6787 | test accuracy: 27.250 | epoch runtime:   5.52 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:19:03,782 - INFO - Evaluate Summary Time 1.72s\tLoss 3.6850\t Acc@1 27.2100\t Acc@5 50.0400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:19:03,782 - INFO - Head 43.750\tMid 26.657\tTail 7.345\u001b[0m\n",
      "\u001b[32m2024-10-06 15:19:03,783 - INFO - epoch:  53 | train loss: 4.4624 | train accuracy: 75.900 | test loss: 3.6850 | test accuracy: 27.210 | epoch runtime:   5.40 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:19:09,307 - INFO - Evaluate Summary Time 1.79s\tLoss 3.6714\t Acc@1 27.3900\t Acc@5 50.0600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:19:09,308 - INFO - Head 42.500\tMid 27.629\tTail 8.345\u001b[0m\n",
      "\u001b[32m2024-10-06 15:19:09,308 - INFO - epoch:  54 | train loss: 4.4548 | train accuracy: 77.259 | test loss: 3.6714 | test accuracy: 27.390 | epoch runtime:   5.53 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:19:14,693 - INFO - Evaluate Summary Time 1.72s\tLoss 3.6647\t Acc@1 27.2800\t Acc@5 50.1500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:19:14,694 - INFO - Head 43.417\tMid 26.686\tTail 7.966\u001b[0m\n",
      "\u001b[32m2024-10-06 15:19:14,694 - INFO - epoch:  55 | train loss: 4.4498 | train accuracy: 78.281 | test loss: 3.6647 | test accuracy: 27.280 | epoch runtime:   5.39 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:19:20,165 - INFO - Evaluate Summary Time 1.73s\tLoss 3.6940\t Acc@1 27.1200\t Acc@5 48.9100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:19:20,166 - INFO - Head 43.167\tMid 26.771\tTail 7.621\u001b[0m\n",
      "\u001b[32m2024-10-06 15:19:20,166 - INFO - epoch:  56 | train loss: 4.4423 | train accuracy: 79.778 | test loss: 3.6940 | test accuracy: 27.120 | epoch runtime:   5.47 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:19:25,667 - INFO - Evaluate Summary Time 1.77s\tLoss 3.6758\t Acc@1 26.9700\t Acc@5 49.1900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:19:25,667 - INFO - Head 42.694\tMid 26.914\tTail 7.517\u001b[0m\n",
      "\u001b[32m2024-10-06 15:19:25,667 - INFO - epoch:  57 | train loss: 4.4359 | train accuracy: 81.270 | test loss: 3.6758 | test accuracy: 26.970 | epoch runtime:   5.50 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:19:31,090 - INFO - Evaluate Summary Time 1.72s\tLoss 3.7078\t Acc@1 26.3900\t Acc@5 49.1900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:19:31,090 - INFO - Head 41.889\tMid 26.657\tTail 6.828\u001b[0m\n",
      "\u001b[32m2024-10-06 15:19:31,091 - INFO - epoch:  58 | train loss: 4.4289 | train accuracy: 82.512 | test loss: 3.7078 | test accuracy: 26.390 | epoch runtime:   5.42 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:19:36,569 - INFO - Evaluate Summary Time 1.78s\tLoss 3.6584\t Acc@1 27.0400\t Acc@5 49.4800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:19:36,570 - INFO - Head 42.361\tMid 27.400\tTail 7.586\u001b[0m\n",
      "\u001b[32m2024-10-06 15:19:36,570 - INFO - epoch:  59 | train loss: 4.4241 | train accuracy: 83.544 | test loss: 3.6584 | test accuracy: 27.040 | epoch runtime:   5.48 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:19:42,091 - INFO - Evaluate Summary Time 1.78s\tLoss 3.6992\t Acc@1 26.7600\t Acc@5 49.0900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:19:42,092 - INFO - Head 42.750\tMid 26.514\tTail 7.207\u001b[0m\n",
      "\u001b[32m2024-10-06 15:19:42,092 - INFO - epoch:  60 | train loss: 4.4197 | train accuracy: 84.509 | test loss: 3.6992 | test accuracy: 26.760 | epoch runtime:   5.52 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:19:47,603 - INFO - Evaluate Summary Time 1.74s\tLoss 3.6647\t Acc@1 27.4900\t Acc@5 49.6500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:19:47,603 - INFO - Head 43.361\tMid 27.229\tTail 8.103\u001b[0m\n",
      "\u001b[32m2024-10-06 15:19:47,603 - INFO - epoch:  61 | train loss: 4.4124 | train accuracy: 85.643 | test loss: 3.6647 | test accuracy: 27.490 | epoch runtime:   5.51 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:19:53,028 - INFO - Evaluate Summary Time 1.64s\tLoss 3.6652\t Acc@1 26.8400\t Acc@5 49.2700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:19:53,029 - INFO - Head 42.556\tMid 26.600\tTail 7.621\u001b[0m\n",
      "\u001b[32m2024-10-06 15:19:53,029 - INFO - epoch:  62 | train loss: 4.4073 | train accuracy: 86.532 | test loss: 3.6652 | test accuracy: 26.840 | epoch runtime:   5.43 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:19:58,512 - INFO - Evaluate Summary Time 1.75s\tLoss 3.6616\t Acc@1 27.2200\t Acc@5 49.2700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:19:58,513 - INFO - Head 42.444\tMid 27.429\tTail 8.069\u001b[0m\n",
      "\u001b[32m2024-10-06 15:19:58,513 - INFO - epoch:  63 | train loss: 4.4035 | train accuracy: 87.043 | test loss: 3.6616 | test accuracy: 27.220 | epoch runtime:   5.48 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:20:03,982 - INFO - Evaluate Summary Time 1.76s\tLoss 3.6545\t Acc@1 26.9400\t Acc@5 49.4100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:20:03,982 - INFO - Head 41.472\tMid 27.086\tTail 8.724\u001b[0m\n",
      "\u001b[32m2024-10-06 15:20:03,983 - INFO - epoch:  64 | train loss: 4.4008 | train accuracy: 87.805 | test loss: 3.6545 | test accuracy: 26.940 | epoch runtime:   5.47 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:20:09,427 - INFO - Evaluate Summary Time 1.73s\tLoss 3.6607\t Acc@1 26.7600\t Acc@5 49.2300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:20:09,427 - INFO - Head 42.361\tMid 26.600\tTail 7.586\u001b[0m\n",
      "\u001b[32m2024-10-06 15:20:09,427 - INFO - epoch:  65 | train loss: 4.3926 | train accuracy: 88.607 | test loss: 3.6607 | test accuracy: 26.760 | epoch runtime:   5.44 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:20:14,779 - INFO - Evaluate Summary Time 1.68s\tLoss 3.6825\t Acc@1 26.6500\t Acc@5 49.0900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:20:14,779 - INFO - Head 41.778\tMid 26.286\tTail 8.310\u001b[0m\n",
      "\u001b[32m2024-10-06 15:20:14,780 - INFO - epoch:  66 | train loss: 4.3878 | train accuracy: 89.669 | test loss: 3.6825 | test accuracy: 26.650 | epoch runtime:   5.35 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:20:20,262 - INFO - Evaluate Summary Time 1.70s\tLoss 3.6311\t Acc@1 27.2000\t Acc@5 48.5500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:20:20,262 - INFO - Head 41.222\tMid 27.857\tTail 9.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:20:20,263 - INFO - epoch:  67 | train loss: 4.3823 | train accuracy: 90.482 | test loss: 3.6311 | test accuracy: 27.200 | epoch runtime:   5.48 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:20:25,689 - INFO - Evaluate Summary Time 1.75s\tLoss 3.6720\t Acc@1 26.8200\t Acc@5 49.0200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:20:25,689 - INFO - Head 42.361\tMid 26.171\tTail 8.310\u001b[0m\n",
      "\u001b[32m2024-10-06 15:20:25,690 - INFO - epoch:  68 | train loss: 4.3782 | train accuracy: 91.146 | test loss: 3.6720 | test accuracy: 26.820 | epoch runtime:   5.43 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:20:31,131 - INFO - Evaluate Summary Time 1.78s\tLoss 3.6580\t Acc@1 26.9500\t Acc@5 48.8400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:20:31,132 - INFO - Head 40.639\tMid 28.286\tTail 8.345\u001b[0m\n",
      "\u001b[32m2024-10-06 15:20:31,132 - INFO - epoch:  69 | train loss: 4.3743 | train accuracy: 91.785 | test loss: 3.6580 | test accuracy: 26.950 | epoch runtime:   5.44 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:20:36,708 - INFO - Evaluate Summary Time 1.81s\tLoss 3.6547\t Acc@1 26.8600\t Acc@5 48.4900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:20:36,708 - INFO - Head 42.111\tMid 26.657\tTail 8.172\u001b[0m\n",
      "\u001b[32m2024-10-06 15:20:36,709 - INFO - epoch:  70 | train loss: 4.3715 | train accuracy: 91.902 | test loss: 3.6547 | test accuracy: 26.860 | epoch runtime:   5.58 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:20:42,145 - INFO - Evaluate Summary Time 1.74s\tLoss 3.6581\t Acc@1 26.7800\t Acc@5 48.7200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:20:42,146 - INFO - Head 41.583\tMid 27.000\tTail 8.138\u001b[0m\n",
      "\u001b[32m2024-10-06 15:20:42,146 - INFO - epoch:  71 | train loss: 4.3656 | train accuracy: 92.679 | test loss: 3.6581 | test accuracy: 26.780 | epoch runtime:   5.44 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:20:47,706 - INFO - Evaluate Summary Time 1.77s\tLoss 3.6225\t Acc@1 26.9800\t Acc@5 48.8700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:20:47,706 - INFO - Head 41.278\tMid 27.429\tTail 8.690\u001b[0m\n",
      "\u001b[32m2024-10-06 15:20:47,707 - INFO - epoch:  72 | train loss: 4.3643 | train accuracy: 93.215 | test loss: 3.6225 | test accuracy: 26.980 | epoch runtime:   5.56 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:20:53,135 - INFO - Evaluate Summary Time 1.78s\tLoss 3.6714\t Acc@1 26.6900\t Acc@5 48.5600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:20:53,136 - INFO - Head 42.000\tMid 26.771\tTail 7.586\u001b[0m\n",
      "\u001b[32m2024-10-06 15:20:53,136 - INFO - epoch:  73 | train loss: 4.3597 | train accuracy: 93.660 | test loss: 3.6714 | test accuracy: 26.690 | epoch runtime:   5.43 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:20:58,589 - INFO - Evaluate Summary Time 1.74s\tLoss 3.6694\t Acc@1 26.6600\t Acc@5 48.1900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:20:58,589 - INFO - Head 41.972\tMid 27.000\tTail 7.241\u001b[0m\n",
      "\u001b[32m2024-10-06 15:20:58,590 - INFO - epoch:  74 | train loss: 4.3573 | train accuracy: 93.992 | test loss: 3.6694 | test accuracy: 26.660 | epoch runtime:   5.45 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:21:04,241 - INFO - Evaluate Summary Time 1.79s\tLoss 3.6605\t Acc@1 27.4200\t Acc@5 48.6300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:21:04,242 - INFO - Head 41.694\tMid 27.943\tTail 9.069\u001b[0m\n",
      "\u001b[32m2024-10-06 15:21:04,242 - INFO - epoch:  75 | train loss: 4.3527 | train accuracy: 94.278 | test loss: 3.6605 | test accuracy: 27.420 | epoch runtime:   5.65 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:21:09,730 - INFO - Evaluate Summary Time 1.74s\tLoss 3.6700\t Acc@1 26.8700\t Acc@5 48.8000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:21:09,730 - INFO - Head 41.750\tMid 27.029\tTail 8.207\u001b[0m\n",
      "\u001b[32m2024-10-06 15:21:09,730 - INFO - epoch:  76 | train loss: 4.3513 | train accuracy: 94.881 | test loss: 3.6700 | test accuracy: 26.870 | epoch runtime:   5.49 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:21:15,368 - INFO - Evaluate Summary Time 1.78s\tLoss 3.6494\t Acc@1 26.9200\t Acc@5 48.8900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:21:15,368 - INFO - Head 41.250\tMid 27.600\tTail 8.310\u001b[0m\n",
      "\u001b[32m2024-10-06 15:21:15,368 - INFO - epoch:  77 | train loss: 4.3476 | train accuracy: 95.116 | test loss: 3.6494 | test accuracy: 26.920 | epoch runtime:   5.64 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:21:20,883 - INFO - Evaluate Summary Time 1.66s\tLoss 3.6669\t Acc@1 26.5400\t Acc@5 47.9700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:21:20,883 - INFO - Head 41.389\tMid 26.429\tTail 8.241\u001b[0m\n",
      "\u001b[32m2024-10-06 15:21:20,884 - INFO - epoch:  78 | train loss: 4.3436 | train accuracy: 95.576 | test loss: 3.6669 | test accuracy: 26.540 | epoch runtime:   5.52 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:21:26,412 - INFO - Evaluate Summary Time 1.70s\tLoss 3.6504\t Acc@1 27.0600\t Acc@5 48.4400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:21:26,412 - INFO - Head 41.611\tMid 27.429\tTail 8.552\u001b[0m\n",
      "\u001b[32m2024-10-06 15:21:26,413 - INFO - epoch:  79 | train loss: 4.3413 | train accuracy: 95.693 | test loss: 3.6504 | test accuracy: 27.060 | epoch runtime:   5.53 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:21:31,924 - INFO - Evaluate Summary Time 1.59s\tLoss 3.6419\t Acc@1 26.8100\t Acc@5 47.7200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:21:31,924 - INFO - Head 40.972\tMid 27.143\tTail 8.828\u001b[0m\n",
      "\u001b[32m2024-10-06 15:21:31,925 - INFO - epoch:  80 | train loss: 4.3365 | train accuracy: 96.020 | test loss: 3.6419 | test accuracy: 26.810 | epoch runtime:   5.51 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:21:37,404 - INFO - Evaluate Summary Time 1.70s\tLoss 3.6366\t Acc@1 27.2200\t Acc@5 48.3500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:21:37,405 - INFO - Head 41.556\tMid 27.771\tTail 8.759\u001b[0m\n",
      "\u001b[32m2024-10-06 15:21:37,405 - INFO - epoch:  81 | train loss: 4.3151 | train accuracy: 97.476 | test loss: 3.6366 | test accuracy: 27.220 | epoch runtime:   5.48 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:21:42,855 - INFO - Evaluate Summary Time 1.75s\tLoss 3.6513\t Acc@1 26.9700\t Acc@5 47.8800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:21:42,855 - INFO - Head 41.139\tMid 27.371\tTail 8.897\u001b[0m\n",
      "\u001b[32m2024-10-06 15:21:42,856 - INFO - epoch:  82 | train loss: 4.3084 | train accuracy: 97.829 | test loss: 3.6513 | test accuracy: 26.970 | epoch runtime:   5.45 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:21:48,301 - INFO - Evaluate Summary Time 1.69s\tLoss 3.6352\t Acc@1 26.8900\t Acc@5 47.9400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:21:48,302 - INFO - Head 40.861\tMid 27.600\tTail 8.690\u001b[0m\n",
      "\u001b[32m2024-10-06 15:21:48,302 - INFO - epoch:  83 | train loss: 4.3046 | train accuracy: 98.002 | test loss: 3.6352 | test accuracy: 26.890 | epoch runtime:   5.45 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:21:53,671 - INFO - Evaluate Summary Time 1.73s\tLoss 3.6555\t Acc@1 27.0200\t Acc@5 48.4300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:21:53,671 - INFO - Head 41.306\tMid 27.657\tTail 8.517\u001b[0m\n",
      "\u001b[32m2024-10-06 15:21:53,672 - INFO - epoch:  84 | train loss: 4.3048 | train accuracy: 98.156 | test loss: 3.6555 | test accuracy: 27.020 | epoch runtime:   5.37 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:21:59,120 - INFO - Evaluate Summary Time 1.76s\tLoss 3.6553\t Acc@1 26.5100\t Acc@5 48.1400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:21:59,121 - INFO - Head 40.972\tMid 26.800\tTail 8.207\u001b[0m\n",
      "\u001b[32m2024-10-06 15:21:59,121 - INFO - epoch:  85 | train loss: 4.3001 | train accuracy: 98.263 | test loss: 3.6553 | test accuracy: 26.510 | epoch runtime:   5.45 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:22:04,577 - INFO - Evaluate Summary Time 1.78s\tLoss 3.6352\t Acc@1 26.9000\t Acc@5 47.7700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:22:04,578 - INFO - Head 40.750\tMid 27.486\tTail 9.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:22:04,578 - INFO - epoch:  86 | train loss: 4.2977 | train accuracy: 98.329 | test loss: 3.6352 | test accuracy: 26.900 | epoch runtime:   5.46 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:22:10,181 - INFO - Evaluate Summary Time 1.81s\tLoss 3.6451\t Acc@1 26.5900\t Acc@5 48.1400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:22:10,181 - INFO - Head 40.444\tMid 27.543\tTail 8.241\u001b[0m\n",
      "\u001b[32m2024-10-06 15:22:10,182 - INFO - epoch:  87 | train loss: 4.2969 | train accuracy: 98.539 | test loss: 3.6451 | test accuracy: 26.590 | epoch runtime:   5.60 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:22:15,597 - INFO - Evaluate Summary Time 1.72s\tLoss 3.6366\t Acc@1 27.2000\t Acc@5 48.1600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:22:15,597 - INFO - Head 41.250\tMid 27.771\tTail 9.069\u001b[0m\n",
      "\u001b[32m2024-10-06 15:22:15,598 - INFO - epoch:  88 | train loss: 4.2955 | train accuracy: 98.580 | test loss: 3.6366 | test accuracy: 27.200 | epoch runtime:   5.42 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:22:21,068 - INFO - Evaluate Summary Time 1.71s\tLoss 3.6437\t Acc@1 26.7000\t Acc@5 47.6500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:22:21,068 - INFO - Head 40.472\tMid 27.286\tTail 8.897\u001b[0m\n",
      "\u001b[32m2024-10-06 15:22:21,068 - INFO - epoch:  89 | train loss: 4.2936 | train accuracy: 98.534 | test loss: 3.6437 | test accuracy: 26.700 | epoch runtime:   5.47 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:22:26,476 - INFO - Evaluate Summary Time 1.76s\tLoss 3.6201\t Acc@1 27.1200\t Acc@5 47.9800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:22:26,476 - INFO - Head 40.889\tMid 27.600\tTail 9.448\u001b[0m\n",
      "\u001b[32m2024-10-06 15:22:26,477 - INFO - epoch:  90 | train loss: 4.2929 | train accuracy: 98.789 | test loss: 3.6201 | test accuracy: 27.120 | epoch runtime:   5.41 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:22:31,875 - INFO - Evaluate Summary Time 1.76s\tLoss 3.6456\t Acc@1 26.6000\t Acc@5 47.6800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:22:31,875 - INFO - Head 40.806\tMid 26.571\tTail 9.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:22:31,876 - INFO - epoch:  91 | train loss: 4.2922 | train accuracy: 98.789 | test loss: 3.6456 | test accuracy: 26.600 | epoch runtime:   5.40 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:22:37,464 - INFO - Evaluate Summary Time 1.84s\tLoss 3.6476\t Acc@1 26.5700\t Acc@5 47.9600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:22:37,464 - INFO - Head 40.556\tMid 27.257\tTail 8.379\u001b[0m\n",
      "\u001b[32m2024-10-06 15:22:37,465 - INFO - epoch:  92 | train loss: 4.2896 | train accuracy: 98.789 | test loss: 3.6476 | test accuracy: 26.570 | epoch runtime:   5.59 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:22:42,812 - INFO - Evaluate Summary Time 1.63s\tLoss 3.6585\t Acc@1 26.6700\t Acc@5 47.9200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:22:42,812 - INFO - Head 40.806\tMid 27.086\tTail 8.621\u001b[0m\n",
      "\u001b[32m2024-10-06 15:22:42,813 - INFO - epoch:  93 | train loss: 4.2876 | train accuracy: 98.994 | test loss: 3.6585 | test accuracy: 26.670 | epoch runtime:   5.35 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:22:48,270 - INFO - Evaluate Summary Time 1.71s\tLoss 3.6329\t Acc@1 27.0800\t Acc@5 48.1300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:22:48,270 - INFO - Head 40.500\tMid 27.657\tTail 9.724\u001b[0m\n",
      "\u001b[32m2024-10-06 15:22:48,271 - INFO - epoch:  94 | train loss: 4.2859 | train accuracy: 98.963 | test loss: 3.6329 | test accuracy: 27.080 | epoch runtime:   5.46 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:22:53,694 - INFO - Evaluate Summary Time 1.76s\tLoss 3.6529\t Acc@1 26.5400\t Acc@5 47.6400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:22:53,695 - INFO - Head 40.389\tMid 26.943\tTail 8.862\u001b[0m\n",
      "\u001b[32m2024-10-06 15:22:53,695 - INFO - epoch:  95 | train loss: 4.2842 | train accuracy: 99.055 | test loss: 3.6529 | test accuracy: 26.540 | epoch runtime:   5.42 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:22:59,121 - INFO - Evaluate Summary Time 1.72s\tLoss 3.6447\t Acc@1 27.0400\t Acc@5 48.1400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:22:59,122 - INFO - Head 40.861\tMid 27.800\tTail 8.966\u001b[0m\n",
      "\u001b[32m2024-10-06 15:22:59,122 - INFO - epoch:  96 | train loss: 4.2813 | train accuracy: 99.111 | test loss: 3.6447 | test accuracy: 27.040 | epoch runtime:   5.43 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:23:04,530 - INFO - Evaluate Summary Time 1.74s\tLoss 3.6470\t Acc@1 26.5400\t Acc@5 47.7600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:23:04,530 - INFO - Head 40.278\tMid 27.029\tTail 8.897\u001b[0m\n",
      "\u001b[32m2024-10-06 15:23:04,530 - INFO - epoch:  97 | train loss: 4.2821 | train accuracy: 99.080 | test loss: 3.6470 | test accuracy: 26.540 | epoch runtime:   5.41 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:23:10,018 - INFO - Evaluate Summary Time 1.72s\tLoss 3.6489\t Acc@1 26.9000\t Acc@5 47.8900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:23:10,018 - INFO - Head 40.917\tMid 27.543\tTail 8.724\u001b[0m\n",
      "\u001b[32m2024-10-06 15:23:10,019 - INFO - epoch:  98 | train loss: 4.2803 | train accuracy: 99.244 | test loss: 3.6489 | test accuracy: 26.900 | epoch runtime:   5.49 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:23:15,489 - INFO - Evaluate Summary Time 1.80s\tLoss 3.6707\t Acc@1 26.5300\t Acc@5 47.8600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:23:15,490 - INFO - Head 40.278\tMid 26.800\tTail 9.138\u001b[0m\n",
      "\u001b[32m2024-10-06 15:23:15,490 - INFO - epoch:  99 | train loss: 4.2794 | train accuracy: 99.142 | test loss: 3.6707 | test accuracy: 26.530 | epoch runtime:   5.47 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:23:20,900 - INFO - Evaluate Summary Time 1.72s\tLoss 3.6366\t Acc@1 26.6600\t Acc@5 47.8800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:23:20,901 - INFO - Head 40.250\tMid 27.114\tTail 9.241\u001b[0m\n",
      "\u001b[32m2024-10-06 15:23:20,901 - INFO - epoch: 100 | train loss: 4.2800 | train accuracy: 99.218 | test loss: 3.6366 | test accuracy: 26.660 | epoch runtime:   5.41 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:23:26,342 - INFO - Evaluate Summary Time 1.74s\tLoss 3.6302\t Acc@1 26.6500\t Acc@5 47.7500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:23:26,342 - INFO - Head 39.139\tMid 27.771\tTail 9.793\u001b[0m\n",
      "\u001b[32m2024-10-06 15:23:26,343 - INFO - epoch: 101 | train loss: 4.2781 | train accuracy: 99.254 | test loss: 3.6302 | test accuracy: 26.650 | epoch runtime:   5.44 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:23:31,688 - INFO - Evaluate Summary Time 1.69s\tLoss 3.6527\t Acc@1 26.5800\t Acc@5 47.6900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:23:31,689 - INFO - Head 40.556\tMid 26.543\tTail 9.276\u001b[0m\n",
      "\u001b[32m2024-10-06 15:23:31,689 - INFO - epoch: 102 | train loss: 4.2757 | train accuracy: 99.269 | test loss: 3.6527 | test accuracy: 26.580 | epoch runtime:   5.35 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:23:37,241 - INFO - Evaluate Summary Time 1.82s\tLoss 3.6529\t Acc@1 26.7600\t Acc@5 48.1900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:23:37,242 - INFO - Head 41.000\tMid 27.143\tTail 8.621\u001b[0m\n",
      "\u001b[32m2024-10-06 15:23:37,242 - INFO - epoch: 103 | train loss: 4.2754 | train accuracy: 99.341 | test loss: 3.6529 | test accuracy: 26.760 | epoch runtime:   5.55 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:23:42,566 - INFO - Evaluate Summary Time 1.66s\tLoss 3.6536\t Acc@1 27.0000\t Acc@5 47.5100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:23:42,566 - INFO - Head 40.528\tMid 27.657\tTail 9.414\u001b[0m\n",
      "\u001b[32m2024-10-06 15:23:42,567 - INFO - epoch: 104 | train loss: 4.2740 | train accuracy: 99.382 | test loss: 3.6536 | test accuracy: 27.000 | epoch runtime:   5.32 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:23:48,061 - INFO - Evaluate Summary Time 1.71s\tLoss 3.6493\t Acc@1 26.7400\t Acc@5 47.4500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:23:48,061 - INFO - Head 40.222\tMid 27.400\tTail 9.207\u001b[0m\n",
      "\u001b[32m2024-10-06 15:23:48,062 - INFO - epoch: 105 | train loss: 4.2735 | train accuracy: 99.453 | test loss: 3.6493 | test accuracy: 26.740 | epoch runtime:   5.49 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:23:53,462 - INFO - Evaluate Summary Time 1.72s\tLoss 3.6447\t Acc@1 26.6900\t Acc@5 47.7700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:23:53,462 - INFO - Head 40.194\tMid 27.343\tTail 9.138\u001b[0m\n",
      "\u001b[32m2024-10-06 15:23:53,462 - INFO - epoch: 106 | train loss: 4.2711 | train accuracy: 99.412 | test loss: 3.6447 | test accuracy: 26.690 | epoch runtime:   5.40 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:23:58,934 - INFO - Evaluate Summary Time 1.70s\tLoss 3.6485\t Acc@1 26.7400\t Acc@5 47.7900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:23:58,934 - INFO - Head 40.583\tMid 27.086\tTail 9.138\u001b[0m\n",
      "\u001b[32m2024-10-06 15:23:58,934 - INFO - epoch: 107 | train loss: 4.2699 | train accuracy: 99.397 | test loss: 3.6485 | test accuracy: 26.740 | epoch runtime:   5.47 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:24:04,437 - INFO - Evaluate Summary Time 1.80s\tLoss 3.6410\t Acc@1 26.8800\t Acc@5 47.6400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:24:04,437 - INFO - Head 39.611\tMid 27.914\tTail 9.828\u001b[0m\n",
      "\u001b[32m2024-10-06 15:24:04,437 - INFO - epoch: 108 | train loss: 4.2717 | train accuracy: 99.361 | test loss: 3.6410 | test accuracy: 26.880 | epoch runtime:   5.50 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:24:09,926 - INFO - Evaluate Summary Time 1.78s\tLoss 3.6498\t Acc@1 26.8400\t Acc@5 47.6300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:24:09,927 - INFO - Head 40.278\tMid 27.457\tTail 9.414\u001b[0m\n",
      "\u001b[32m2024-10-06 15:24:09,927 - INFO - epoch: 109 | train loss: 4.2696 | train accuracy: 99.392 | test loss: 3.6498 | test accuracy: 26.840 | epoch runtime:   5.49 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:24:15,179 - INFO - Evaluate Summary Time 1.67s\tLoss 3.6437\t Acc@1 26.9500\t Acc@5 48.0500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:24:15,179 - INFO - Head 40.417\tMid 27.343\tTail 9.759\u001b[0m\n",
      "\u001b[32m2024-10-06 15:24:15,180 - INFO - epoch: 110 | train loss: 4.2683 | train accuracy: 99.428 | test loss: 3.6437 | test accuracy: 26.950 | epoch runtime:   5.25 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:24:20,563 - INFO - Evaluate Summary Time 1.74s\tLoss 3.6385\t Acc@1 26.8200\t Acc@5 47.5300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:24:20,564 - INFO - Head 39.972\tMid 27.857\tTail 9.241\u001b[0m\n",
      "\u001b[32m2024-10-06 15:24:20,564 - INFO - epoch: 111 | train loss: 4.2681 | train accuracy: 99.525 | test loss: 3.6385 | test accuracy: 26.820 | epoch runtime:   5.38 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:24:25,922 - INFO - Evaluate Summary Time 1.74s\tLoss 3.6476\t Acc@1 26.4700\t Acc@5 47.3900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:24:25,923 - INFO - Head 39.806\tMid 26.943\tTail 9.345\u001b[0m\n",
      "\u001b[32m2024-10-06 15:24:25,923 - INFO - epoch: 112 | train loss: 4.2668 | train accuracy: 99.591 | test loss: 3.6476 | test accuracy: 26.470 | epoch runtime:   5.36 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:24:31,343 - INFO - Evaluate Summary Time 1.74s\tLoss 3.6557\t Acc@1 27.0000\t Acc@5 47.7200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:24:31,343 - INFO - Head 41.139\tMid 27.314\tTail 9.069\u001b[0m\n",
      "\u001b[32m2024-10-06 15:24:31,344 - INFO - epoch: 113 | train loss: 4.2654 | train accuracy: 99.469 | test loss: 3.6557 | test accuracy: 27.000 | epoch runtime:   5.42 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:24:36,850 - INFO - Evaluate Summary Time 1.78s\tLoss 3.6437\t Acc@1 26.7500\t Acc@5 47.7000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:24:36,850 - INFO - Head 40.194\tMid 27.457\tTail 9.207\u001b[0m\n",
      "\u001b[32m2024-10-06 15:24:36,850 - INFO - epoch: 114 | train loss: 4.2659 | train accuracy: 99.510 | test loss: 3.6437 | test accuracy: 26.750 | epoch runtime:   5.51 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:24:42,299 - INFO - Evaluate Summary Time 1.68s\tLoss 3.6346\t Acc@1 27.0400\t Acc@5 47.6500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:24:42,300 - INFO - Head 40.472\tMid 27.629\tTail 9.655\u001b[0m\n",
      "\u001b[32m2024-10-06 15:24:42,300 - INFO - epoch: 115 | train loss: 4.2645 | train accuracy: 99.504 | test loss: 3.6346 | test accuracy: 27.040 | epoch runtime:   5.45 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:24:47,738 - INFO - Evaluate Summary Time 1.69s\tLoss 3.6342\t Acc@1 26.8400\t Acc@5 47.5700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:24:47,738 - INFO - Head 39.639\tMid 27.857\tTail 9.724\u001b[0m\n",
      "\u001b[32m2024-10-06 15:24:47,738 - INFO - epoch: 116 | train loss: 4.2628 | train accuracy: 99.535 | test loss: 3.6342 | test accuracy: 26.840 | epoch runtime:   5.44 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:24:53,062 - INFO - Evaluate Summary Time 1.69s\tLoss 3.6483\t Acc@1 26.7900\t Acc@5 47.3900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:24:53,063 - INFO - Head 40.028\tMid 27.429\tTail 9.586\u001b[0m\n",
      "\u001b[32m2024-10-06 15:24:53,063 - INFO - epoch: 117 | train loss: 4.2641 | train accuracy: 99.556 | test loss: 3.6483 | test accuracy: 26.790 | epoch runtime:   5.32 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:24:58,470 - INFO - Evaluate Summary Time 1.64s\tLoss 3.6454\t Acc@1 26.5700\t Acc@5 47.3200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:24:58,470 - INFO - Head 39.778\tMid 27.171\tTail 9.448\u001b[0m\n",
      "\u001b[32m2024-10-06 15:24:58,471 - INFO - epoch: 118 | train loss: 4.2615 | train accuracy: 99.550 | test loss: 3.6454 | test accuracy: 26.570 | epoch runtime:   5.41 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:25:03,817 - INFO - Evaluate Summary Time 1.72s\tLoss 3.6580\t Acc@1 26.6900\t Acc@5 47.2500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:25:03,818 - INFO - Head 40.194\tMid 27.200\tTail 9.310\u001b[0m\n",
      "\u001b[32m2024-10-06 15:25:03,818 - INFO - epoch: 119 | train loss: 4.2610 | train accuracy: 99.601 | test loss: 3.6580 | test accuracy: 26.690 | epoch runtime:   5.35 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:25:09,324 - INFO - Evaluate Summary Time 1.75s\tLoss 3.6459\t Acc@1 26.7300\t Acc@5 47.4400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:25:09,324 - INFO - Head 40.000\tMid 27.429\tTail 9.414\u001b[0m\n",
      "\u001b[32m2024-10-06 15:25:09,324 - INFO - epoch: 120 | train loss: 4.2621 | train accuracy: 99.576 | test loss: 3.6459 | test accuracy: 26.730 | epoch runtime:   5.51 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:25:14,773 - INFO - Evaluate Summary Time 1.76s\tLoss 3.6427\t Acc@1 26.8500\t Acc@5 47.4300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:25:14,774 - INFO - Head 40.389\tMid 27.486\tTail 9.276\u001b[0m\n",
      "\u001b[32m2024-10-06 15:25:14,774 - INFO - epoch: 121 | train loss: 4.2613 | train accuracy: 99.586 | test loss: 3.6427 | test accuracy: 26.850 | epoch runtime:   5.45 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:25:20,188 - INFO - Evaluate Summary Time 1.67s\tLoss 3.6458\t Acc@1 26.7200\t Acc@5 47.3300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:25:20,188 - INFO - Head 39.806\tMid 27.429\tTail 9.621\u001b[0m\n",
      "\u001b[32m2024-10-06 15:25:20,189 - INFO - epoch: 122 | train loss: 4.2611 | train accuracy: 99.632 | test loss: 3.6458 | test accuracy: 26.720 | epoch runtime:   5.41 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:25:25,646 - INFO - Evaluate Summary Time 1.77s\tLoss 3.6566\t Acc@1 26.9000\t Acc@5 47.5900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:25:25,647 - INFO - Head 40.333\tMid 27.457\tTail 9.552\u001b[0m\n",
      "\u001b[32m2024-10-06 15:25:25,647 - INFO - epoch: 123 | train loss: 4.2582 | train accuracy: 99.607 | test loss: 3.6566 | test accuracy: 26.900 | epoch runtime:   5.46 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:25:31,057 - INFO - Evaluate Summary Time 1.68s\tLoss 3.6381\t Acc@1 27.0000\t Acc@5 47.3800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:25:31,057 - INFO - Head 39.750\tMid 28.143\tTail 9.793\u001b[0m\n",
      "\u001b[32m2024-10-06 15:25:31,057 - INFO - epoch: 124 | train loss: 4.2591 | train accuracy: 99.658 | test loss: 3.6381 | test accuracy: 27.000 | epoch runtime:   5.41 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:25:36,671 - INFO - Evaluate Summary Time 1.87s\tLoss 3.6447\t Acc@1 26.7300\t Acc@5 47.2500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:25:36,672 - INFO - Head 39.694\tMid 27.457\tTail 9.759\u001b[0m\n",
      "\u001b[32m2024-10-06 15:25:36,672 - INFO - epoch: 125 | train loss: 4.2580 | train accuracy: 99.591 | test loss: 3.6447 | test accuracy: 26.730 | epoch runtime:   5.61 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:25:42,069 - INFO - Evaluate Summary Time 1.70s\tLoss 3.6541\t Acc@1 26.6300\t Acc@5 47.5000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:25:42,070 - INFO - Head 40.167\tMid 27.143\tTail 9.207\u001b[0m\n",
      "\u001b[32m2024-10-06 15:25:42,070 - INFO - epoch: 126 | train loss: 4.2577 | train accuracy: 99.673 | test loss: 3.6541 | test accuracy: 26.630 | epoch runtime:   5.40 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:25:47,605 - INFO - Evaluate Summary Time 1.76s\tLoss 3.6452\t Acc@1 26.5900\t Acc@5 47.4600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:25:47,605 - INFO - Head 39.639\tMid 27.371\tTail 9.448\u001b[0m\n",
      "\u001b[32m2024-10-06 15:25:47,605 - INFO - epoch: 127 | train loss: 4.2580 | train accuracy: 99.653 | test loss: 3.6452 | test accuracy: 26.590 | epoch runtime:   5.54 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:25:52,994 - INFO - Evaluate Summary Time 1.69s\tLoss 3.6675\t Acc@1 26.4900\t Acc@5 47.4600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:25:52,995 - INFO - Head 40.472\tMid 27.000\tTail 8.517\u001b[0m\n",
      "\u001b[32m2024-10-06 15:25:52,995 - INFO - epoch: 128 | train loss: 4.2567 | train accuracy: 99.678 | test loss: 3.6675 | test accuracy: 26.490 | epoch runtime:   5.39 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:25:58,522 - INFO - Evaluate Summary Time 1.74s\tLoss 3.6609\t Acc@1 26.7100\t Acc@5 47.3700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:25:58,522 - INFO - Head 40.333\tMid 27.486\tTail 8.862\u001b[0m\n",
      "\u001b[32m2024-10-06 15:25:58,522 - INFO - epoch: 129 | train loss: 4.2548 | train accuracy: 99.612 | test loss: 3.6609 | test accuracy: 26.710 | epoch runtime:   5.53 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:26:04,002 - INFO - Evaluate Summary Time 1.81s\tLoss 3.6560\t Acc@1 26.7000\t Acc@5 47.5300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:26:04,002 - INFO - Head 40.083\tMid 27.229\tTail 9.448\u001b[0m\n",
      "\u001b[32m2024-10-06 15:26:04,002 - INFO - epoch: 130 | train loss: 4.2566 | train accuracy: 99.663 | test loss: 3.6560 | test accuracy: 26.700 | epoch runtime:   5.48 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:26:09,464 - INFO - Evaluate Summary Time 1.72s\tLoss 3.6412\t Acc@1 26.5200\t Acc@5 47.4100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:26:09,464 - INFO - Head 39.389\tMid 27.371\tTail 9.517\u001b[0m\n",
      "\u001b[32m2024-10-06 15:26:09,465 - INFO - epoch: 131 | train loss: 4.2545 | train accuracy: 99.663 | test loss: 3.6412 | test accuracy: 26.520 | epoch runtime:   5.46 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:26:14,976 - INFO - Evaluate Summary Time 1.79s\tLoss 3.6497\t Acc@1 26.8900\t Acc@5 47.5300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:26:14,977 - INFO - Head 39.833\tMid 27.943\tTail 9.552\u001b[0m\n",
      "\u001b[32m2024-10-06 15:26:14,977 - INFO - epoch: 132 | train loss: 4.2552 | train accuracy: 99.699 | test loss: 3.6497 | test accuracy: 26.890 | epoch runtime:   5.51 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:26:20,347 - INFO - Evaluate Summary Time 1.70s\tLoss 3.6632\t Acc@1 26.8400\t Acc@5 47.6700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:26:20,347 - INFO - Head 40.500\tMid 27.400\tTail 9.207\u001b[0m\n",
      "\u001b[32m2024-10-06 15:26:20,348 - INFO - epoch: 133 | train loss: 4.2561 | train accuracy: 99.709 | test loss: 3.6632 | test accuracy: 26.840 | epoch runtime:   5.37 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:26:25,772 - INFO - Evaluate Summary Time 1.75s\tLoss 3.6439\t Acc@1 26.5300\t Acc@5 47.4200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:26:25,772 - INFO - Head 40.028\tMid 27.086\tTail 9.103\u001b[0m\n",
      "\u001b[32m2024-10-06 15:26:25,773 - INFO - epoch: 134 | train loss: 4.2559 | train accuracy: 99.627 | test loss: 3.6439 | test accuracy: 26.530 | epoch runtime:   5.42 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:26:31,162 - INFO - Evaluate Summary Time 1.71s\tLoss 3.6331\t Acc@1 26.6400\t Acc@5 47.6300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:26:31,162 - INFO - Head 39.111\tMid 27.971\tTail 9.552\u001b[0m\n",
      "\u001b[32m2024-10-06 15:26:31,162 - INFO - epoch: 135 | train loss: 4.2548 | train accuracy: 99.699 | test loss: 3.6331 | test accuracy: 26.640 | epoch runtime:   5.39 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:26:36,622 - INFO - Evaluate Summary Time 1.72s\tLoss 3.6667\t Acc@1 26.4600\t Acc@5 47.6100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:26:36,622 - INFO - Head 40.000\tMid 26.943\tTail 9.069\u001b[0m\n",
      "\u001b[32m2024-10-06 15:26:36,622 - INFO - epoch: 136 | train loss: 4.2542 | train accuracy: 99.668 | test loss: 3.6667 | test accuracy: 26.460 | epoch runtime:   5.46 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:26:41,968 - INFO - Evaluate Summary Time 1.68s\tLoss 3.6482\t Acc@1 26.6100\t Acc@5 47.5400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:26:41,968 - INFO - Head 39.889\tMid 27.457\tTail 9.103\u001b[0m\n",
      "\u001b[32m2024-10-06 15:26:41,969 - INFO - epoch: 137 | train loss: 4.2542 | train accuracy: 99.627 | test loss: 3.6482 | test accuracy: 26.610 | epoch runtime:   5.35 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:26:47,358 - INFO - Evaluate Summary Time 1.72s\tLoss 3.6385\t Acc@1 26.8900\t Acc@5 47.4700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:26:47,359 - INFO - Head 39.806\tMid 27.600\tTail 10.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:26:47,359 - INFO - epoch: 138 | train loss: 4.2542 | train accuracy: 99.678 | test loss: 3.6385 | test accuracy: 26.890 | epoch runtime:   5.39 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:26:52,877 - INFO - Evaluate Summary Time 1.83s\tLoss 3.6469\t Acc@1 26.7000\t Acc@5 47.2500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:26:52,878 - INFO - Head 40.111\tMid 27.143\tTail 9.517\u001b[0m\n",
      "\u001b[32m2024-10-06 15:26:52,878 - INFO - epoch: 139 | train loss: 4.2528 | train accuracy: 99.714 | test loss: 3.6469 | test accuracy: 26.700 | epoch runtime:   5.52 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:26:58,485 - INFO - Evaluate Summary Time 1.80s\tLoss 3.6407\t Acc@1 26.6500\t Acc@5 47.4400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:26:58,485 - INFO - Head 39.389\tMid 27.600\tTail 9.690\u001b[0m\n",
      "\u001b[32m2024-10-06 15:26:58,486 - INFO - epoch: 140 | train loss: 4.2542 | train accuracy: 99.683 | test loss: 3.6407 | test accuracy: 26.650 | epoch runtime:   5.61 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:27:03,840 - INFO - Evaluate Summary Time 1.73s\tLoss 3.6589\t Acc@1 26.7000\t Acc@5 47.3200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:27:03,841 - INFO - Head 40.028\tMid 27.514\tTail 9.172\u001b[0m\n",
      "\u001b[32m2024-10-06 15:27:03,841 - INFO - epoch: 141 | train loss: 4.2561 | train accuracy: 99.734 | test loss: 3.6589 | test accuracy: 26.700 | epoch runtime:   5.36 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:27:09,273 - INFO - Evaluate Summary Time 1.70s\tLoss 3.6490\t Acc@1 26.6900\t Acc@5 47.4400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:27:09,273 - INFO - Head 39.694\tMid 27.457\tTail 9.621\u001b[0m\n",
      "\u001b[32m2024-10-06 15:27:09,274 - INFO - epoch: 142 | train loss: 4.2536 | train accuracy: 99.699 | test loss: 3.6490 | test accuracy: 26.690 | epoch runtime:   5.43 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:27:14,708 - INFO - Evaluate Summary Time 1.72s\tLoss 3.6508\t Acc@1 26.7400\t Acc@5 47.4400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:27:14,708 - INFO - Head 39.917\tMid 27.571\tTail 9.379\u001b[0m\n",
      "\u001b[32m2024-10-06 15:27:14,709 - INFO - epoch: 143 | train loss: 4.2539 | train accuracy: 99.683 | test loss: 3.6508 | test accuracy: 26.740 | epoch runtime:   5.43 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:27:20,131 - INFO - Evaluate Summary Time 1.71s\tLoss 3.6455\t Acc@1 26.5900\t Acc@5 47.2100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:27:20,131 - INFO - Head 40.028\tMid 27.114\tTail 9.276\u001b[0m\n",
      "\u001b[32m2024-10-06 15:27:20,132 - INFO - epoch: 144 | train loss: 4.2546 | train accuracy: 99.658 | test loss: 3.6455 | test accuracy: 26.590 | epoch runtime:   5.42 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:27:25,652 - INFO - Evaluate Summary Time 1.74s\tLoss 3.6371\t Acc@1 26.7700\t Acc@5 47.5000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:27:25,653 - INFO - Head 39.694\tMid 27.686\tTail 9.621\u001b[0m\n",
      "\u001b[32m2024-10-06 15:27:25,653 - INFO - epoch: 145 | train loss: 4.2538 | train accuracy: 99.704 | test loss: 3.6371 | test accuracy: 26.770 | epoch runtime:   5.52 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:27:31,064 - INFO - Evaluate Summary Time 1.73s\tLoss 3.6516\t Acc@1 26.8400\t Acc@5 47.4800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:27:31,064 - INFO - Head 39.972\tMid 27.629\tTail 9.586\u001b[0m\n",
      "\u001b[32m2024-10-06 15:27:31,065 - INFO - epoch: 146 | train loss: 4.2532 | train accuracy: 99.663 | test loss: 3.6516 | test accuracy: 26.840 | epoch runtime:   5.41 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:27:36,668 - INFO - Evaluate Summary Time 1.84s\tLoss 3.6460\t Acc@1 26.7100\t Acc@5 47.1200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:27:36,669 - INFO - Head 39.639\tMid 27.600\tTail 9.586\u001b[0m\n",
      "\u001b[32m2024-10-06 15:27:36,669 - INFO - epoch: 147 | train loss: 4.2533 | train accuracy: 99.719 | test loss: 3.6460 | test accuracy: 26.710 | epoch runtime:   5.60 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:27:42,098 - INFO - Evaluate Summary Time 1.72s\tLoss 3.6472\t Acc@1 26.8600\t Acc@5 47.4900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:27:42,099 - INFO - Head 40.222\tMid 27.714\tTail 9.241\u001b[0m\n",
      "\u001b[32m2024-10-06 15:27:42,099 - INFO - epoch: 148 | train loss: 4.2538 | train accuracy: 99.704 | test loss: 3.6472 | test accuracy: 26.860 | epoch runtime:   5.43 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:27:47,567 - INFO - Evaluate Summary Time 1.73s\tLoss 3.6346\t Acc@1 26.5300\t Acc@5 47.0600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:27:47,568 - INFO - Head 39.194\tMid 27.600\tTail 9.517\u001b[0m\n",
      "\u001b[32m2024-10-06 15:27:47,568 - INFO - epoch: 149 | train loss: 4.2518 | train accuracy: 99.683 | test loss: 3.6346 | test accuracy: 26.530 | epoch runtime:   5.47 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:27:52,970 - INFO - Evaluate Summary Time 1.69s\tLoss 3.6509\t Acc@1 26.8400\t Acc@5 47.7100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:27:52,970 - INFO - Head 39.944\tMid 27.771\tTail 9.448\u001b[0m\n",
      "\u001b[32m2024-10-06 15:27:52,970 - INFO - epoch: 150 | train loss: 4.2533 | train accuracy: 99.673 | test loss: 3.6509 | test accuracy: 26.840 | epoch runtime:   5.40 sec | best accuracy: 28.180 @ epoch: 037\u001b[0m\n",
      "Runtime of this script /home/zyx/zhengjinpeng/PNP/cifar.py : 820.1 seconds (0.228 hours)\n",
      "Config:\n",
      "{\n",
      "    database: Datasets\n",
      "    dataset: cifar100\n",
      "    n_classes: 100\n",
      "    rescale_size: 32\n",
      "    crop_size: 32\n",
      "    cfg_file: ./config/cifar100.cfg\n",
      "    synthetic_data: cifar80no\n",
      "    noise_type: asymmetric\n",
      "    closeset_ratio: 0.2\n",
      "    r_ood: 0.2\n",
      "    r_imb: 0.1\n",
      "    gpu: 0\n",
      "    net: cnn\n",
      "    batch_size: 128\n",
      "    lr: 0.001\n",
      "    lr_decay: cosine\n",
      "    weight_decay: 1e-05\n",
      "    opt: adam\n",
      "    warmup_epochs: 5\n",
      "    warmup_lr_scale: 10.0\n",
      "    epochs: 150\n",
      "    save_model: False\n",
      "    use_fp16: False\n",
      "    use_grad_accumulate: False\n",
      "    project: \n",
      "    log: PENIOC\n",
      "    epsilon: 0.5\n",
      "    temperature: 0.1\n",
      "    eta: 0.5\n",
      "    alpha: 0.0\n",
      "    beta: 1.0\n",
      "    gamma: 1.0\n",
      "    omega: 0.1\n",
      "    rho: 1.0\n",
      "    loss_func_aux: mae\n",
      "    weighting: soft\n",
      "    neg_cons: False\n",
      "    activation: tanh\n",
      "    ablation: False\n",
      "    log_freq: 1\n",
      "    asym: True\n",
      "}\n",
      "\n",
      "Available GPUs Index : 0\n",
      "using CIFAR-100...\n",
      "Built imbalanced dataset, r_imb=0.1\n",
      "Mixing in OOD noise, r_ood=0.2\n",
      "[ 0  0  0 ... 99 99 99]\n",
      "Actual noise 0.20\n",
      "Mixing in ID asym noise, r_id=0.2\n",
      "using CIFAR-100...\n",
      "\u001b[32m2024-10-06 15:28:00,027 - INFO - Categories: 100, Training Samples: 19573, Testing Samples: 10000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:28:00,027 - INFO - Optimizer: adam\u001b[0m\n",
      "\u001b[32m2024-10-06 15:28:00,027 - INFO - Accumulate gradients every 1 iterations --> Acutal batch size is 128\u001b[0m\n",
      "\u001b[32m2024-10-06 15:28:05,828 - INFO - Evaluate Summary Time 1.84s\tLoss 4.3571\t Acc@1 6.8100\t Acc@5 19.8700\u001b[0m                                        \n",
      "\u001b[32m2024-10-06 15:28:05,829 - INFO - Head 16.889\tMid 1.914\tTail 0.207\u001b[0m\n",
      "\u001b[32m2024-10-06 15:28:05,829 - INFO - epoch:   1 | train loss: 4.6548 | train accuracy:  5.288 | test loss: 4.3571 | test accuracy:  6.810 | epoch runtime:   5.80 sec | best accuracy:  6.810 @ epoch: 001\u001b[0m\n",
      "\u001b[32m2024-10-06 15:28:10,750 - INFO - Evaluate Summary Time 1.74s\tLoss 4.1822\t Acc@1 9.3900\t Acc@5 26.4100\u001b[0m                                        \n",
      "\u001b[32m2024-10-06 15:28:10,751 - INFO - Head 20.917\tMid 5.171\tTail 0.172\u001b[0m\n",
      "\u001b[32m2024-10-06 15:28:10,751 - INFO - epoch:   2 | train loss: 4.4757 | train accuracy:  9.554 | test loss: 4.1822 | test accuracy:  9.390 | epoch runtime:   4.92 sec | best accuracy:  9.390 @ epoch: 002\u001b[0m\n",
      "\u001b[32m2024-10-06 15:28:15,663 - INFO - Evaluate Summary Time 1.69s\tLoss 4.0468\t Acc@1 11.8300\t Acc@5 30.9200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:28:15,663 - INFO - Head 24.972\tMid 7.543\tTail 0.690\u001b[0m\n",
      "\u001b[32m2024-10-06 15:28:15,663 - INFO - epoch:   3 | train loss: 4.4260 | train accuracy: 11.930 | test loss: 4.0468 | test accuracy: 11.830 | epoch runtime:   4.91 sec | best accuracy: 11.830 @ epoch: 003\u001b[0m\n",
      "\u001b[32m2024-10-06 15:28:20,574 - INFO - Evaluate Summary Time 1.78s\tLoss 4.0033\t Acc@1 13.8200\t Acc@5 34.6500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:28:20,575 - INFO - Head 26.722\tMid 9.800\tTail 2.655\u001b[0m\n",
      "\u001b[32m2024-10-06 15:28:20,575 - INFO - epoch:   4 | train loss: 4.3812 | train accuracy: 14.397 | test loss: 4.0033 | test accuracy: 13.820 | epoch runtime:   4.91 sec | best accuracy: 13.820 @ epoch: 004\u001b[0m\n",
      "\u001b[32m2024-10-06 15:28:25,507 - INFO - Evaluate Summary Time 1.73s\tLoss 3.8562\t Acc@1 16.4700\t Acc@5 39.1400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:28:25,507 - INFO - Head 30.028\tMid 14.657\tTail 1.828\u001b[0m\n",
      "\u001b[32m2024-10-06 15:28:25,508 - INFO - epoch:   5 | train loss: 4.3368 | train accuracy: 16.921 | test loss: 3.8562 | test accuracy: 16.470 | epoch runtime:   4.93 sec | best accuracy: 16.470 @ epoch: 005\u001b[0m\n",
      "\u001b[32m2024-10-06 15:28:31,068 - INFO - Evaluate Summary Time 1.76s\tLoss 3.9181\t Acc@1 18.8500\t Acc@5 43.3800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:28:31,069 - INFO - Head 33.778\tMid 16.571\tTail 3.069\u001b[0m\n",
      "\u001b[32m2024-10-06 15:28:31,069 - INFO - epoch:   6 | train loss: 4.9571 | train accuracy: 20.630 | test loss: 3.9181 | test accuracy: 18.850 | epoch runtime:   5.56 sec | best accuracy: 18.850 @ epoch: 006\u001b[0m\n",
      "\u001b[32m2024-10-06 15:28:36,480 - INFO - Evaluate Summary Time 1.70s\tLoss 3.8569\t Acc@1 19.6100\t Acc@5 44.9200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:28:36,480 - INFO - Head 35.139\tMid 17.457\tTail 2.931\u001b[0m\n",
      "\u001b[32m2024-10-06 15:28:36,480 - INFO - epoch:   7 | train loss: 4.8931 | train accuracy: 22.311 | test loss: 3.8569 | test accuracy: 19.610 | epoch runtime:   5.41 sec | best accuracy: 19.610 @ epoch: 007\u001b[0m\n",
      "\u001b[32m2024-10-06 15:28:41,746 - INFO - Evaluate Summary Time 1.66s\tLoss 3.8617\t Acc@1 19.9700\t Acc@5 44.9700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:28:41,746 - INFO - Head 36.694\tMid 17.114\tTail 2.655\u001b[0m\n",
      "\u001b[32m2024-10-06 15:28:41,746 - INFO - epoch:   8 | train loss: 4.8570 | train accuracy: 23.088 | test loss: 3.8617 | test accuracy: 19.970 | epoch runtime:   5.27 sec | best accuracy: 19.970 @ epoch: 008\u001b[0m\n",
      "\u001b[32m2024-10-06 15:28:47,176 - INFO - Evaluate Summary Time 1.74s\tLoss 3.8497\t Acc@1 21.1000\t Acc@5 45.6800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:28:47,176 - INFO - Head 38.944\tMid 18.429\tTail 2.172\u001b[0m\n",
      "\u001b[32m2024-10-06 15:28:47,177 - INFO - epoch:   9 | train loss: 4.8321 | train accuracy: 23.716 | test loss: 3.8497 | test accuracy: 21.100 | epoch runtime:   5.43 sec | best accuracy: 21.100 @ epoch: 009\u001b[0m\n",
      "\u001b[32m2024-10-06 15:28:52,581 - INFO - Evaluate Summary Time 1.73s\tLoss 3.9035\t Acc@1 21.1900\t Acc@5 45.9600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:28:52,581 - INFO - Head 38.222\tMid 18.486\tTail 3.310\u001b[0m\n",
      "\u001b[32m2024-10-06 15:28:52,581 - INFO - epoch:  10 | train loss: 4.7989 | train accuracy: 24.360 | test loss: 3.9035 | test accuracy: 21.190 | epoch runtime:   5.40 sec | best accuracy: 21.190 @ epoch: 010\u001b[0m\n",
      "\u001b[32m2024-10-06 15:28:58,036 - INFO - Evaluate Summary Time 1.69s\tLoss 3.9321\t Acc@1 21.8900\t Acc@5 46.7200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:28:58,036 - INFO - Head 38.528\tMid 20.029\tTail 3.483\u001b[0m\n",
      "\u001b[32m2024-10-06 15:28:58,037 - INFO - epoch:  11 | train loss: 4.7769 | train accuracy: 25.709 | test loss: 3.9321 | test accuracy: 21.890 | epoch runtime:   5.46 sec | best accuracy: 21.890 @ epoch: 011\u001b[0m\n",
      "\u001b[32m2024-10-06 15:29:03,350 - INFO - Evaluate Summary Time 1.69s\tLoss 3.8658\t Acc@1 22.5800\t Acc@5 47.7500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:29:03,350 - INFO - Head 38.861\tMid 20.886\tTail 4.414\u001b[0m\n",
      "\u001b[32m2024-10-06 15:29:03,350 - INFO - epoch:  12 | train loss: 4.7623 | train accuracy: 26.746 | test loss: 3.8658 | test accuracy: 22.580 | epoch runtime:   5.31 sec | best accuracy: 22.580 @ epoch: 012\u001b[0m\n",
      "\u001b[32m2024-10-06 15:29:08,667 - INFO - Evaluate Summary Time 1.72s\tLoss 3.9886\t Acc@1 22.5400\t Acc@5 47.1800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:29:08,667 - INFO - Head 39.250\tMid 19.914\tTail 4.966\u001b[0m\n",
      "\u001b[32m2024-10-06 15:29:08,667 - INFO - epoch:  13 | train loss: 4.7509 | train accuracy: 27.374 | test loss: 3.9886 | test accuracy: 22.540 | epoch runtime:   5.32 sec | best accuracy: 22.580 @ epoch: 012\u001b[0m\n",
      "\u001b[32m2024-10-06 15:29:14,041 - INFO - Evaluate Summary Time 1.73s\tLoss 3.9340\t Acc@1 23.4100\t Acc@5 48.6600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:29:14,042 - INFO - Head 40.139\tMid 21.314\tTail 5.172\u001b[0m\n",
      "\u001b[32m2024-10-06 15:29:14,042 - INFO - epoch:  14 | train loss: 4.7399 | train accuracy: 28.391 | test loss: 3.9340 | test accuracy: 23.410 | epoch runtime:   5.37 sec | best accuracy: 23.410 @ epoch: 014\u001b[0m\n",
      "\u001b[32m2024-10-06 15:29:19,481 - INFO - Evaluate Summary Time 1.75s\tLoss 3.8441\t Acc@1 24.4300\t Acc@5 49.9900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:29:19,481 - INFO - Head 41.194\tMid 22.057\tTail 6.483\u001b[0m\n",
      "\u001b[32m2024-10-06 15:29:19,481 - INFO - epoch:  15 | train loss: 4.7298 | train accuracy: 29.617 | test loss: 3.8441 | test accuracy: 24.430 | epoch runtime:   5.44 sec | best accuracy: 24.430 @ epoch: 015\u001b[0m\n",
      "\u001b[32m2024-10-06 15:29:24,977 - INFO - Evaluate Summary Time 1.82s\tLoss 3.9355\t Acc@1 23.8500\t Acc@5 49.2200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:29:24,977 - INFO - Head 40.667\tMid 21.714\tTail 5.552\u001b[0m\n",
      "\u001b[32m2024-10-06 15:29:24,978 - INFO - epoch:  16 | train loss: 4.7226 | train accuracy: 30.511 | test loss: 3.9355 | test accuracy: 23.850 | epoch runtime:   5.50 sec | best accuracy: 24.430 @ epoch: 015\u001b[0m\n",
      "\u001b[32m2024-10-06 15:29:30,352 - INFO - Evaluate Summary Time 1.76s\tLoss 3.8767\t Acc@1 25.3300\t Acc@5 50.6400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:29:30,352 - INFO - Head 41.972\tMid 23.771\tTail 6.552\u001b[0m\n",
      "\u001b[32m2024-10-06 15:29:30,352 - INFO - epoch:  17 | train loss: 4.7101 | train accuracy: 31.605 | test loss: 3.8767 | test accuracy: 25.330 | epoch runtime:   5.37 sec | best accuracy: 25.330 @ epoch: 017\u001b[0m\n",
      "\u001b[32m2024-10-06 15:29:35,858 - INFO - Evaluate Summary Time 1.80s\tLoss 3.8611\t Acc@1 24.3900\t Acc@5 50.5100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:29:35,858 - INFO - Head 41.500\tMid 22.057\tTail 5.966\u001b[0m\n",
      "\u001b[32m2024-10-06 15:29:35,858 - INFO - epoch:  18 | train loss: 4.7005 | train accuracy: 32.575 | test loss: 3.8611 | test accuracy: 24.390 | epoch runtime:   5.51 sec | best accuracy: 25.330 @ epoch: 017\u001b[0m\n",
      "\u001b[32m2024-10-06 15:29:41,299 - INFO - Evaluate Summary Time 1.69s\tLoss 3.8440\t Acc@1 25.4700\t Acc@5 50.8900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:29:41,299 - INFO - Head 42.222\tMid 23.800\tTail 6.690\u001b[0m\n",
      "\u001b[32m2024-10-06 15:29:41,299 - INFO - epoch:  19 | train loss: 4.6916 | train accuracy: 33.715 | test loss: 3.8440 | test accuracy: 25.470 | epoch runtime:   5.44 sec | best accuracy: 25.470 @ epoch: 019\u001b[0m\n",
      "\u001b[32m2024-10-06 15:29:46,720 - INFO - Evaluate Summary Time 1.71s\tLoss 3.8153\t Acc@1 26.1900\t Acc@5 52.1500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:29:46,720 - INFO - Head 42.833\tMid 24.629\tTail 7.414\u001b[0m\n",
      "\u001b[32m2024-10-06 15:29:46,721 - INFO - epoch:  20 | train loss: 4.6805 | train accuracy: 35.074 | test loss: 3.8153 | test accuracy: 26.190 | epoch runtime:   5.42 sec | best accuracy: 26.190 @ epoch: 020\u001b[0m\n",
      "\u001b[32m2024-10-06 15:29:52,106 - INFO - Evaluate Summary Time 1.64s\tLoss 3.8105\t Acc@1 25.9200\t Acc@5 51.5500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:29:52,106 - INFO - Head 42.444\tMid 24.886\tTail 6.655\u001b[0m\n",
      "\u001b[32m2024-10-06 15:29:52,106 - INFO - epoch:  21 | train loss: 4.6710 | train accuracy: 36.428 | test loss: 3.8105 | test accuracy: 25.920 | epoch runtime:   5.39 sec | best accuracy: 26.190 @ epoch: 020\u001b[0m\n",
      "\u001b[32m2024-10-06 15:29:57,609 - INFO - Evaluate Summary Time 1.73s\tLoss 3.7370\t Acc@1 26.5500\t Acc@5 52.1900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:29:57,609 - INFO - Head 43.750\tMid 23.943\tTail 8.345\u001b[0m\n",
      "\u001b[32m2024-10-06 15:29:57,609 - INFO - epoch:  22 | train loss: 4.6599 | train accuracy: 37.322 | test loss: 3.7370 | test accuracy: 26.550 | epoch runtime:   5.50 sec | best accuracy: 26.550 @ epoch: 022\u001b[0m\n",
      "\u001b[32m2024-10-06 15:30:02,959 - INFO - Evaluate Summary Time 1.67s\tLoss 3.7467\t Acc@1 26.9700\t Acc@5 52.8700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:30:02,960 - INFO - Head 43.889\tMid 25.371\tTail 7.897\u001b[0m\n",
      "\u001b[32m2024-10-06 15:30:02,960 - INFO - epoch:  23 | train loss: 4.6491 | train accuracy: 39.207 | test loss: 3.7467 | test accuracy: 26.970 | epoch runtime:   5.35 sec | best accuracy: 26.970 @ epoch: 023\u001b[0m\n",
      "\u001b[32m2024-10-06 15:30:08,361 - INFO - Evaluate Summary Time 1.65s\tLoss 3.7268\t Acc@1 26.9900\t Acc@5 52.9200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:30:08,362 - INFO - Head 44.083\tMid 25.457\tTail 7.621\u001b[0m\n",
      "\u001b[32m2024-10-06 15:30:08,362 - INFO - epoch:  24 | train loss: 4.6424 | train accuracy: 40.326 | test loss: 3.7268 | test accuracy: 26.990 | epoch runtime:   5.40 sec | best accuracy: 26.990 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 15:30:13,765 - INFO - Evaluate Summary Time 1.81s\tLoss 3.6773\t Acc@1 27.6500\t Acc@5 53.2600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:30:13,765 - INFO - Head 45.583\tMid 25.886\tTail 7.517\u001b[0m\n",
      "\u001b[32m2024-10-06 15:30:13,765 - INFO - epoch:  25 | train loss: 4.6301 | train accuracy: 41.608 | test loss: 3.6773 | test accuracy: 27.650 | epoch runtime:   5.40 sec | best accuracy: 27.650 @ epoch: 025\u001b[0m\n",
      "\u001b[32m2024-10-06 15:30:19,227 - INFO - Evaluate Summary Time 1.77s\tLoss 3.7094\t Acc@1 27.6600\t Acc@5 53.0100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:30:19,228 - INFO - Head 44.833\tMid 26.114\tTail 8.207\u001b[0m\n",
      "\u001b[32m2024-10-06 15:30:19,228 - INFO - epoch:  26 | train loss: 4.6198 | train accuracy: 43.371 | test loss: 3.7094 | test accuracy: 27.660 | epoch runtime:   5.46 sec | best accuracy: 27.660 @ epoch: 026\u001b[0m\n",
      "\u001b[32m2024-10-06 15:30:24,704 - INFO - Evaluate Summary Time 1.84s\tLoss 3.6881\t Acc@1 27.5500\t Acc@5 53.4900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:30:24,704 - INFO - Head 45.528\tMid 25.086\tTail 8.207\u001b[0m\n",
      "\u001b[32m2024-10-06 15:30:24,704 - INFO - epoch:  27 | train loss: 4.6102 | train accuracy: 44.745 | test loss: 3.6881 | test accuracy: 27.550 | epoch runtime:   5.48 sec | best accuracy: 27.660 @ epoch: 026\u001b[0m\n",
      "\u001b[32m2024-10-06 15:30:30,140 - INFO - Evaluate Summary Time 1.69s\tLoss 3.6752\t Acc@1 27.9500\t Acc@5 53.7100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:30:30,141 - INFO - Head 45.472\tMid 26.743\tTail 7.655\u001b[0m\n",
      "\u001b[32m2024-10-06 15:30:30,141 - INFO - epoch:  28 | train loss: 4.6011 | train accuracy: 46.104 | test loss: 3.6752 | test accuracy: 27.950 | epoch runtime:   5.44 sec | best accuracy: 27.950 @ epoch: 028\u001b[0m\n",
      "\u001b[32m2024-10-06 15:30:35,648 - INFO - Evaluate Summary Time 1.79s\tLoss 3.6726\t Acc@1 27.8800\t Acc@5 53.4300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:30:35,648 - INFO - Head 45.056\tMid 26.743\tTail 7.931\u001b[0m\n",
      "\u001b[32m2024-10-06 15:30:35,648 - INFO - epoch:  29 | train loss: 4.5904 | train accuracy: 47.801 | test loss: 3.6726 | test accuracy: 27.880 | epoch runtime:   5.51 sec | best accuracy: 27.950 @ epoch: 028\u001b[0m\n",
      "\u001b[32m2024-10-06 15:30:41,103 - INFO - Evaluate Summary Time 1.68s\tLoss 3.6426\t Acc@1 28.1300\t Acc@5 53.5200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:30:41,104 - INFO - Head 45.611\tMid 26.629\tTail 8.241\u001b[0m\n",
      "\u001b[32m2024-10-06 15:30:41,104 - INFO - epoch:  30 | train loss: 4.5813 | train accuracy: 49.298 | test loss: 3.6426 | test accuracy: 28.130 | epoch runtime:   5.46 sec | best accuracy: 28.130 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 15:30:46,543 - INFO - Evaluate Summary Time 1.72s\tLoss 3.6281\t Acc@1 28.1000\t Acc@5 53.7000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:30:46,543 - INFO - Head 45.222\tMid 27.029\tTail 8.138\u001b[0m\n",
      "\u001b[32m2024-10-06 15:30:46,543 - INFO - epoch:  31 | train loss: 4.5721 | train accuracy: 50.815 | test loss: 3.6281 | test accuracy: 28.100 | epoch runtime:   5.44 sec | best accuracy: 28.130 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 15:30:51,871 - INFO - Evaluate Summary Time 1.69s\tLoss 3.5971\t Acc@1 28.1100\t Acc@5 53.7100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:30:51,872 - INFO - Head 45.389\tMid 26.686\tTail 8.379\u001b[0m\n",
      "\u001b[32m2024-10-06 15:30:51,872 - INFO - epoch:  32 | train loss: 4.5623 | train accuracy: 53.012 | test loss: 3.5971 | test accuracy: 28.110 | epoch runtime:   5.33 sec | best accuracy: 28.130 @ epoch: 030\u001b[0m\n",
      "\u001b[32m2024-10-06 15:30:57,228 - INFO - Evaluate Summary Time 1.71s\tLoss 3.5859\t Acc@1 28.8900\t Acc@5 54.1400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:30:57,228 - INFO - Head 45.889\tMid 28.257\tTail 8.552\u001b[0m\n",
      "\u001b[32m2024-10-06 15:30:57,229 - INFO - epoch:  33 | train loss: 4.5519 | train accuracy: 54.672 | test loss: 3.5859 | test accuracy: 28.890 | epoch runtime:   5.36 sec | best accuracy: 28.890 @ epoch: 033\u001b[0m\n",
      "\u001b[32m2024-10-06 15:31:02,469 - INFO - Evaluate Summary Time 1.64s\tLoss 3.6202\t Acc@1 28.8200\t Acc@5 53.5500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:31:02,469 - INFO - Head 45.806\tMid 28.086\tTail 8.621\u001b[0m\n",
      "\u001b[32m2024-10-06 15:31:02,470 - INFO - epoch:  34 | train loss: 4.5434 | train accuracy: 56.271 | test loss: 3.6202 | test accuracy: 28.820 | epoch runtime:   5.24 sec | best accuracy: 28.890 @ epoch: 033\u001b[0m\n",
      "\u001b[32m2024-10-06 15:31:07,994 - INFO - Evaluate Summary Time 1.77s\tLoss 3.5978\t Acc@1 28.5300\t Acc@5 53.9300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:31:07,994 - INFO - Head 46.000\tMid 27.286\tTail 8.345\u001b[0m\n",
      "\u001b[32m2024-10-06 15:31:07,994 - INFO - epoch:  35 | train loss: 4.5314 | train accuracy: 57.896 | test loss: 3.5978 | test accuracy: 28.530 | epoch runtime:   5.52 sec | best accuracy: 28.890 @ epoch: 033\u001b[0m\n",
      "\u001b[32m2024-10-06 15:31:13,404 - INFO - Evaluate Summary Time 1.78s\tLoss 3.6352\t Acc@1 28.6600\t Acc@5 53.6100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:31:13,405 - INFO - Head 46.833\tMid 27.543\tTail 7.448\u001b[0m\n",
      "\u001b[32m2024-10-06 15:31:13,405 - INFO - epoch:  36 | train loss: 4.5235 | train accuracy: 59.516 | test loss: 3.6352 | test accuracy: 28.660 | epoch runtime:   5.41 sec | best accuracy: 28.890 @ epoch: 033\u001b[0m\n",
      "\u001b[32m2024-10-06 15:31:18,811 - INFO - Evaluate Summary Time 1.79s\tLoss 3.5571\t Acc@1 28.8800\t Acc@5 54.3300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:31:18,812 - INFO - Head 45.583\tMid 28.171\tTail 9.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:31:18,812 - INFO - epoch:  37 | train loss: 4.5143 | train accuracy: 61.902 | test loss: 3.5571 | test accuracy: 28.880 | epoch runtime:   5.41 sec | best accuracy: 28.890 @ epoch: 033\u001b[0m\n",
      "\u001b[32m2024-10-06 15:31:24,088 - INFO - Evaluate Summary Time 1.66s\tLoss 3.5777\t Acc@1 28.6800\t Acc@5 54.1300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:31:24,089 - INFO - Head 45.833\tMid 27.800\tTail 8.448\u001b[0m\n",
      "\u001b[32m2024-10-06 15:31:24,089 - INFO - epoch:  38 | train loss: 4.5055 | train accuracy: 62.842 | test loss: 3.5777 | test accuracy: 28.680 | epoch runtime:   5.28 sec | best accuracy: 28.890 @ epoch: 033\u001b[0m\n",
      "\u001b[32m2024-10-06 15:31:29,520 - INFO - Evaluate Summary Time 1.76s\tLoss 3.5914\t Acc@1 28.2800\t Acc@5 53.8100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:31:29,520 - INFO - Head 45.083\tMid 27.229\tTail 8.690\u001b[0m\n",
      "\u001b[32m2024-10-06 15:31:29,521 - INFO - epoch:  39 | train loss: 4.4983 | train accuracy: 64.998 | test loss: 3.5914 | test accuracy: 28.280 | epoch runtime:   5.43 sec | best accuracy: 28.890 @ epoch: 033\u001b[0m\n",
      "\u001b[32m2024-10-06 15:31:34,928 - INFO - Evaluate Summary Time 1.73s\tLoss 3.5779\t Acc@1 28.1000\t Acc@5 53.6500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:31:34,928 - INFO - Head 45.417\tMid 27.143\tTail 7.759\u001b[0m\n",
      "\u001b[32m2024-10-06 15:31:34,929 - INFO - epoch:  40 | train loss: 4.4875 | train accuracy: 66.673 | test loss: 3.5779 | test accuracy: 28.100 | epoch runtime:   5.41 sec | best accuracy: 28.890 @ epoch: 033\u001b[0m\n",
      "\u001b[32m2024-10-06 15:31:40,461 - INFO - Evaluate Summary Time 1.73s\tLoss 3.6099\t Acc@1 28.4300\t Acc@5 53.0100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:31:40,462 - INFO - Head 45.778\tMid 27.029\tTail 8.586\u001b[0m\n",
      "\u001b[32m2024-10-06 15:31:40,462 - INFO - epoch:  41 | train loss: 4.4792 | train accuracy: 68.344 | test loss: 3.6099 | test accuracy: 28.430 | epoch runtime:   5.53 sec | best accuracy: 28.890 @ epoch: 033\u001b[0m\n",
      "\u001b[32m2024-10-06 15:31:45,920 - INFO - Evaluate Summary Time 1.75s\tLoss 3.5414\t Acc@1 28.6100\t Acc@5 53.6900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:31:45,920 - INFO - Head 45.694\tMid 27.229\tTail 9.069\u001b[0m\n",
      "\u001b[32m2024-10-06 15:31:45,921 - INFO - epoch:  42 | train loss: 4.4698 | train accuracy: 70.260 | test loss: 3.5414 | test accuracy: 28.610 | epoch runtime:   5.46 sec | best accuracy: 28.890 @ epoch: 033\u001b[0m\n",
      "\u001b[32m2024-10-06 15:31:51,316 - INFO - Evaluate Summary Time 1.72s\tLoss 3.5320\t Acc@1 28.6800\t Acc@5 53.2600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:31:51,316 - INFO - Head 46.000\tMid 27.371\tTail 8.759\u001b[0m\n",
      "\u001b[32m2024-10-06 15:31:51,317 - INFO - epoch:  43 | train loss: 4.4620 | train accuracy: 71.885 | test loss: 3.5320 | test accuracy: 28.680 | epoch runtime:   5.40 sec | best accuracy: 28.890 @ epoch: 033\u001b[0m\n",
      "\u001b[32m2024-10-06 15:31:56,676 - INFO - Evaluate Summary Time 1.64s\tLoss 3.5594\t Acc@1 28.0200\t Acc@5 53.0600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:31:56,677 - INFO - Head 44.944\tMid 27.429\tTail 7.724\u001b[0m\n",
      "\u001b[32m2024-10-06 15:31:56,677 - INFO - epoch:  44 | train loss: 4.4529 | train accuracy: 73.821 | test loss: 3.5594 | test accuracy: 28.020 | epoch runtime:   5.36 sec | best accuracy: 28.890 @ epoch: 033\u001b[0m\n",
      "\u001b[32m2024-10-06 15:32:02,035 - INFO - Evaluate Summary Time 1.69s\tLoss 3.5432\t Acc@1 28.8700\t Acc@5 53.1200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:32:02,036 - INFO - Head 46.000\tMid 28.057\tTail 8.586\u001b[0m\n",
      "\u001b[32m2024-10-06 15:32:02,036 - INFO - epoch:  45 | train loss: 4.4456 | train accuracy: 75.298 | test loss: 3.5432 | test accuracy: 28.870 | epoch runtime:   5.36 sec | best accuracy: 28.890 @ epoch: 033\u001b[0m\n",
      "\u001b[32m2024-10-06 15:32:07,515 - INFO - Evaluate Summary Time 1.76s\tLoss 3.5663\t Acc@1 28.4400\t Acc@5 53.0500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:32:07,515 - INFO - Head 45.306\tMid 27.600\tTail 8.517\u001b[0m\n",
      "\u001b[32m2024-10-06 15:32:07,516 - INFO - epoch:  46 | train loss: 4.4369 | train accuracy: 76.978 | test loss: 3.5663 | test accuracy: 28.440 | epoch runtime:   5.48 sec | best accuracy: 28.890 @ epoch: 033\u001b[0m\n",
      "\u001b[32m2024-10-06 15:32:12,889 - INFO - Evaluate Summary Time 1.76s\tLoss 3.5687\t Acc@1 28.0300\t Acc@5 53.1500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:32:12,889 - INFO - Head 44.917\tMid 27.114\tTail 8.172\u001b[0m\n",
      "\u001b[32m2024-10-06 15:32:12,889 - INFO - epoch:  47 | train loss: 4.4304 | train accuracy: 78.235 | test loss: 3.5687 | test accuracy: 28.030 | epoch runtime:   5.37 sec | best accuracy: 28.890 @ epoch: 033\u001b[0m\n",
      "\u001b[32m2024-10-06 15:32:18,358 - INFO - Evaluate Summary Time 1.70s\tLoss 3.5530\t Acc@1 28.1800\t Acc@5 52.8900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:32:18,358 - INFO - Head 44.611\tMid 27.914\tTail 8.103\u001b[0m\n",
      "\u001b[32m2024-10-06 15:32:18,359 - INFO - epoch:  48 | train loss: 4.4237 | train accuracy: 79.748 | test loss: 3.5530 | test accuracy: 28.180 | epoch runtime:   5.47 sec | best accuracy: 28.890 @ epoch: 033\u001b[0m\n",
      "\u001b[32m2024-10-06 15:32:23,803 - INFO - Evaluate Summary Time 1.75s\tLoss 3.5421\t Acc@1 28.7600\t Acc@5 53.0800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:32:23,803 - INFO - Head 45.194\tMid 28.943\tTail 8.138\u001b[0m\n",
      "\u001b[32m2024-10-06 15:32:23,804 - INFO - epoch:  49 | train loss: 4.4165 | train accuracy: 81.342 | test loss: 3.5421 | test accuracy: 28.760 | epoch runtime:   5.44 sec | best accuracy: 28.890 @ epoch: 033\u001b[0m\n",
      "\u001b[32m2024-10-06 15:32:29,123 - INFO - Evaluate Summary Time 1.69s\tLoss 3.5358\t Acc@1 28.1000\t Acc@5 52.6200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:32:29,124 - INFO - Head 43.639\tMid 28.600\tTail 8.207\u001b[0m\n",
      "\u001b[32m2024-10-06 15:32:29,124 - INFO - epoch:  50 | train loss: 4.4081 | train accuracy: 82.384 | test loss: 3.5358 | test accuracy: 28.100 | epoch runtime:   5.32 sec | best accuracy: 28.890 @ epoch: 033\u001b[0m\n",
      "\u001b[32m2024-10-06 15:32:34,513 - INFO - Evaluate Summary Time 1.77s\tLoss 3.5611\t Acc@1 28.1600\t Acc@5 52.6000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:32:34,514 - INFO - Head 44.667\tMid 28.057\tTail 7.793\u001b[0m\n",
      "\u001b[32m2024-10-06 15:32:34,514 - INFO - epoch:  51 | train loss: 4.4026 | train accuracy: 84.085 | test loss: 3.5611 | test accuracy: 28.160 | epoch runtime:   5.39 sec | best accuracy: 28.890 @ epoch: 033\u001b[0m\n",
      "\u001b[32m2024-10-06 15:32:40,093 - INFO - Evaluate Summary Time 1.80s\tLoss 3.5162\t Acc@1 29.1100\t Acc@5 53.0400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:32:40,093 - INFO - Head 44.806\tMid 29.857\tTail 8.724\u001b[0m\n",
      "\u001b[32m2024-10-06 15:32:40,093 - INFO - epoch:  52 | train loss: 4.3964 | train accuracy: 84.760 | test loss: 3.5162 | test accuracy: 29.110 | epoch runtime:   5.58 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:32:45,564 - INFO - Evaluate Summary Time 1.75s\tLoss 3.5241\t Acc@1 28.3600\t Acc@5 52.8800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:32:45,565 - INFO - Head 45.083\tMid 27.743\tTail 8.345\u001b[0m\n",
      "\u001b[32m2024-10-06 15:32:45,565 - INFO - epoch:  53 | train loss: 4.3899 | train accuracy: 85.889 | test loss: 3.5241 | test accuracy: 28.360 | epoch runtime:   5.47 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:32:50,985 - INFO - Evaluate Summary Time 1.76s\tLoss 3.5171\t Acc@1 28.3300\t Acc@5 52.6400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:32:50,985 - INFO - Head 43.806\tMid 28.257\tTail 9.207\u001b[0m\n",
      "\u001b[32m2024-10-06 15:32:50,985 - INFO - epoch:  54 | train loss: 4.3818 | train accuracy: 87.350 | test loss: 3.5171 | test accuracy: 28.330 | epoch runtime:   5.42 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:32:56,436 - INFO - Evaluate Summary Time 1.77s\tLoss 3.5278\t Acc@1 28.2700\t Acc@5 52.9000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:32:56,436 - INFO - Head 44.500\tMid 27.800\tTail 8.690\u001b[0m\n",
      "\u001b[32m2024-10-06 15:32:56,436 - INFO - epoch:  55 | train loss: 4.3770 | train accuracy: 87.830 | test loss: 3.5278 | test accuracy: 28.270 | epoch runtime:   5.45 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:33:01,831 - INFO - Evaluate Summary Time 1.78s\tLoss 3.5550\t Acc@1 28.0500\t Acc@5 51.9800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:33:01,831 - INFO - Head 44.250\tMid 27.543\tTail 8.552\u001b[0m\n",
      "\u001b[32m2024-10-06 15:33:01,831 - INFO - epoch:  56 | train loss: 4.3722 | train accuracy: 88.745 | test loss: 3.5550 | test accuracy: 28.050 | epoch runtime:   5.39 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:33:07,271 - INFO - Evaluate Summary Time 1.76s\tLoss 3.5599\t Acc@1 28.1800\t Acc@5 51.2000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:33:07,271 - INFO - Head 44.611\tMid 27.943\tTail 8.069\u001b[0m\n",
      "\u001b[32m2024-10-06 15:33:07,271 - INFO - epoch:  57 | train loss: 4.3643 | train accuracy: 89.613 | test loss: 3.5599 | test accuracy: 28.180 | epoch runtime:   5.44 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:33:12,587 - INFO - Evaluate Summary Time 1.71s\tLoss 3.5138\t Acc@1 28.2900\t Acc@5 52.5000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:33:12,588 - INFO - Head 43.972\tMid 28.286\tTail 8.828\u001b[0m\n",
      "\u001b[32m2024-10-06 15:33:12,588 - INFO - epoch:  58 | train loss: 4.3591 | train accuracy: 90.579 | test loss: 3.5138 | test accuracy: 28.290 | epoch runtime:   5.32 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:33:18,025 - INFO - Evaluate Summary Time 1.77s\tLoss 3.5006\t Acc@1 28.3900\t Acc@5 52.4100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:33:18,026 - INFO - Head 42.583\tMid 29.029\tTail 10.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:33:18,026 - INFO - epoch:  59 | train loss: 4.3559 | train accuracy: 91.447 | test loss: 3.5006 | test accuracy: 28.390 | epoch runtime:   5.44 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:33:23,457 - INFO - Evaluate Summary Time 1.75s\tLoss 3.5557\t Acc@1 28.0700\t Acc@5 51.8000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:33:23,458 - INFO - Head 43.528\tMid 28.429\tTail 8.448\u001b[0m\n",
      "\u001b[32m2024-10-06 15:33:23,458 - INFO - epoch:  60 | train loss: 4.3529 | train accuracy: 92.091 | test loss: 3.5557 | test accuracy: 28.070 | epoch runtime:   5.43 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:33:28,777 - INFO - Evaluate Summary Time 1.67s\tLoss 3.5300\t Acc@1 28.3800\t Acc@5 52.1700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:33:28,778 - INFO - Head 44.028\tMid 28.200\tTail 9.172\u001b[0m\n",
      "\u001b[32m2024-10-06 15:33:28,778 - INFO - epoch:  61 | train loss: 4.3451 | train accuracy: 92.485 | test loss: 3.5300 | test accuracy: 28.380 | epoch runtime:   5.32 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:33:34,227 - INFO - Evaluate Summary Time 1.79s\tLoss 3.5336\t Acc@1 28.1300\t Acc@5 51.5100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:33:34,228 - INFO - Head 44.222\tMid 27.486\tTail 8.931\u001b[0m\n",
      "\u001b[32m2024-10-06 15:33:34,228 - INFO - epoch:  62 | train loss: 4.3429 | train accuracy: 93.098 | test loss: 3.5336 | test accuracy: 28.130 | epoch runtime:   5.45 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:33:39,753 - INFO - Evaluate Summary Time 1.72s\tLoss 3.5330\t Acc@1 28.5400\t Acc@5 52.2100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:33:39,753 - INFO - Head 43.667\tMid 29.286\tTail 8.862\u001b[0m\n",
      "\u001b[32m2024-10-06 15:33:39,753 - INFO - epoch:  63 | train loss: 4.3382 | train accuracy: 93.430 | test loss: 3.5330 | test accuracy: 28.540 | epoch runtime:   5.53 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:33:45,120 - INFO - Evaluate Summary Time 1.69s\tLoss 3.5461\t Acc@1 28.0100\t Acc@5 51.5000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:33:45,120 - INFO - Head 42.667\tMid 28.000\tTail 9.828\u001b[0m\n",
      "\u001b[32m2024-10-06 15:33:45,121 - INFO - epoch:  64 | train loss: 4.3343 | train accuracy: 94.068 | test loss: 3.5461 | test accuracy: 28.010 | epoch runtime:   5.37 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:33:50,440 - INFO - Evaluate Summary Time 1.70s\tLoss 3.5310\t Acc@1 27.8400\t Acc@5 52.0600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:33:50,441 - INFO - Head 42.917\tMid 27.400\tTail 9.655\u001b[0m\n",
      "\u001b[32m2024-10-06 15:33:50,441 - INFO - epoch:  65 | train loss: 4.3294 | train accuracy: 94.533 | test loss: 3.5310 | test accuracy: 27.840 | epoch runtime:   5.32 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:33:55,889 - INFO - Evaluate Summary Time 1.76s\tLoss 3.5302\t Acc@1 27.9800\t Acc@5 51.5900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:33:55,889 - INFO - Head 42.750\tMid 27.800\tTail 9.862\u001b[0m\n",
      "\u001b[32m2024-10-06 15:33:55,889 - INFO - epoch:  66 | train loss: 4.3236 | train accuracy: 95.187 | test loss: 3.5302 | test accuracy: 27.980 | epoch runtime:   5.45 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:34:01,221 - INFO - Evaluate Summary Time 1.67s\tLoss 3.5192\t Acc@1 28.2100\t Acc@5 51.7600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:34:01,221 - INFO - Head 42.500\tMid 29.200\tTail 9.276\u001b[0m\n",
      "\u001b[32m2024-10-06 15:34:01,222 - INFO - epoch:  67 | train loss: 4.3202 | train accuracy: 95.657 | test loss: 3.5192 | test accuracy: 28.210 | epoch runtime:   5.33 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:34:06,610 - INFO - Evaluate Summary Time 1.72s\tLoss 3.5455\t Acc@1 28.6400\t Acc@5 52.1700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:34:06,611 - INFO - Head 43.528\tMid 29.086\tTail 9.621\u001b[0m\n",
      "\u001b[32m2024-10-06 15:34:06,611 - INFO - epoch:  68 | train loss: 4.3172 | train accuracy: 95.897 | test loss: 3.5455 | test accuracy: 28.640 | epoch runtime:   5.39 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:34:12,042 - INFO - Evaluate Summary Time 1.77s\tLoss 3.5378\t Acc@1 28.0400\t Acc@5 51.6900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:34:12,042 - INFO - Head 42.972\tMid 28.143\tTail 9.379\u001b[0m\n",
      "\u001b[32m2024-10-06 15:34:12,042 - INFO - epoch:  69 | train loss: 4.3152 | train accuracy: 95.938 | test loss: 3.5378 | test accuracy: 28.040 | epoch runtime:   5.43 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:34:17,412 - INFO - Evaluate Summary Time 1.60s\tLoss 3.5356\t Acc@1 28.1300\t Acc@5 51.7900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:34:17,413 - INFO - Head 43.083\tMid 28.229\tTail 9.448\u001b[0m\n",
      "\u001b[32m2024-10-06 15:34:17,413 - INFO - epoch:  70 | train loss: 4.3124 | train accuracy: 96.551 | test loss: 3.5356 | test accuracy: 28.130 | epoch runtime:   5.37 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:34:22,746 - INFO - Evaluate Summary Time 1.72s\tLoss 3.5309\t Acc@1 28.0700\t Acc@5 52.1100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:34:22,747 - INFO - Head 41.861\tMid 28.914\tTail 9.931\u001b[0m\n",
      "\u001b[32m2024-10-06 15:34:22,747 - INFO - epoch:  71 | train loss: 4.3077 | train accuracy: 96.705 | test loss: 3.5309 | test accuracy: 28.070 | epoch runtime:   5.33 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:34:28,200 - INFO - Evaluate Summary Time 1.74s\tLoss 3.5104\t Acc@1 28.2900\t Acc@5 50.9500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:34:28,201 - INFO - Head 42.528\tMid 28.143\tTail 10.793\u001b[0m\n",
      "\u001b[32m2024-10-06 15:34:28,201 - INFO - epoch:  72 | train loss: 4.3046 | train accuracy: 97.006 | test loss: 3.5104 | test accuracy: 28.290 | epoch runtime:   5.45 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:34:33,603 - INFO - Evaluate Summary Time 1.76s\tLoss 3.5233\t Acc@1 27.8800\t Acc@5 51.7500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:34:33,603 - INFO - Head 42.639\tMid 27.829\tTail 9.621\u001b[0m\n",
      "\u001b[32m2024-10-06 15:34:33,604 - INFO - epoch:  73 | train loss: 4.3025 | train accuracy: 97.313 | test loss: 3.5233 | test accuracy: 27.880 | epoch runtime:   5.40 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:34:39,201 - INFO - Evaluate Summary Time 1.81s\tLoss 3.5589\t Acc@1 28.0400\t Acc@5 51.2200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:34:39,202 - INFO - Head 42.917\tMid 28.743\tTail 8.724\u001b[0m\n",
      "\u001b[32m2024-10-06 15:34:39,202 - INFO - epoch:  74 | train loss: 4.2997 | train accuracy: 97.297 | test loss: 3.5589 | test accuracy: 28.040 | epoch runtime:   5.60 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:34:44,611 - INFO - Evaluate Summary Time 1.78s\tLoss 3.5349\t Acc@1 28.4900\t Acc@5 51.8900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:34:44,611 - INFO - Head 42.750\tMid 28.771\tTail 10.448\u001b[0m\n",
      "\u001b[32m2024-10-06 15:34:44,611 - INFO - epoch:  75 | train loss: 4.2976 | train accuracy: 97.594 | test loss: 3.5349 | test accuracy: 28.490 | epoch runtime:   5.41 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:34:49,993 - INFO - Evaluate Summary Time 1.66s\tLoss 3.5082\t Acc@1 28.5800\t Acc@5 51.7200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:34:49,993 - INFO - Head 43.083\tMid 28.400\tTail 10.793\u001b[0m\n",
      "\u001b[32m2024-10-06 15:34:49,993 - INFO - epoch:  76 | train loss: 4.2953 | train accuracy: 97.778 | test loss: 3.5082 | test accuracy: 28.580 | epoch runtime:   5.38 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:34:55,583 - INFO - Evaluate Summary Time 1.71s\tLoss 3.5092\t Acc@1 28.3600\t Acc@5 51.5700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:34:55,584 - INFO - Head 42.500\tMid 28.286\tTail 10.897\u001b[0m\n",
      "\u001b[32m2024-10-06 15:34:55,584 - INFO - epoch:  77 | train loss: 4.2909 | train accuracy: 98.069 | test loss: 3.5092 | test accuracy: 28.360 | epoch runtime:   5.59 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:35:01,071 - INFO - Evaluate Summary Time 1.66s\tLoss 3.5329\t Acc@1 28.5800\t Acc@5 51.2600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:35:01,071 - INFO - Head 43.472\tMid 28.343\tTail 10.379\u001b[0m\n",
      "\u001b[32m2024-10-06 15:35:01,072 - INFO - epoch:  78 | train loss: 4.2883 | train accuracy: 98.084 | test loss: 3.5329 | test accuracy: 28.580 | epoch runtime:   5.49 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:35:06,448 - INFO - Evaluate Summary Time 1.70s\tLoss 3.5131\t Acc@1 28.3700\t Acc@5 51.6800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:35:06,448 - INFO - Head 42.194\tMid 28.971\tTail 10.483\u001b[0m\n",
      "\u001b[32m2024-10-06 15:35:06,448 - INFO - epoch:  79 | train loss: 4.2862 | train accuracy: 98.273 | test loss: 3.5131 | test accuracy: 28.370 | epoch runtime:   5.38 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:35:11,862 - INFO - Evaluate Summary Time 1.68s\tLoss 3.5349\t Acc@1 28.2200\t Acc@5 51.0400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:35:11,862 - INFO - Head 41.472\tMid 29.400\tTail 10.345\u001b[0m\n",
      "\u001b[32m2024-10-06 15:35:11,863 - INFO - epoch:  80 | train loss: 4.2846 | train accuracy: 98.324 | test loss: 3.5349 | test accuracy: 28.220 | epoch runtime:   5.41 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:35:17,306 - INFO - Evaluate Summary Time 1.74s\tLoss 3.5116\t Acc@1 28.4900\t Acc@5 51.3300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:35:17,307 - INFO - Head 41.694\tMid 29.057\tTail 11.414\u001b[0m\n",
      "\u001b[32m2024-10-06 15:35:17,307 - INFO - epoch:  81 | train loss: 4.2599 | train accuracy: 99.080 | test loss: 3.5116 | test accuracy: 28.490 | epoch runtime:   5.44 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:35:22,653 - INFO - Evaluate Summary Time 1.70s\tLoss 3.5123\t Acc@1 28.5000\t Acc@5 51.2300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:35:22,654 - INFO - Head 41.750\tMid 29.171\tTail 11.241\u001b[0m\n",
      "\u001b[32m2024-10-06 15:35:22,654 - INFO - epoch:  82 | train loss: 4.2569 | train accuracy: 99.244 | test loss: 3.5123 | test accuracy: 28.500 | epoch runtime:   5.35 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:35:28,047 - INFO - Evaluate Summary Time 1.67s\tLoss 3.5331\t Acc@1 28.3700\t Acc@5 51.0900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:35:28,047 - INFO - Head 42.000\tMid 28.886\tTail 10.828\u001b[0m\n",
      "\u001b[32m2024-10-06 15:35:28,048 - INFO - epoch:  83 | train loss: 4.2514 | train accuracy: 99.315 | test loss: 3.5331 | test accuracy: 28.370 | epoch runtime:   5.39 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:35:33,452 - INFO - Evaluate Summary Time 1.69s\tLoss 3.5024\t Acc@1 28.4700\t Acc@5 51.1900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:35:33,453 - INFO - Head 41.861\tMid 29.343\tTail 10.793\u001b[0m\n",
      "\u001b[32m2024-10-06 15:35:33,453 - INFO - epoch:  84 | train loss: 4.2533 | train accuracy: 99.305 | test loss: 3.5024 | test accuracy: 28.470 | epoch runtime:   5.41 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:35:38,943 - INFO - Evaluate Summary Time 1.74s\tLoss 3.5079\t Acc@1 28.5700\t Acc@5 51.2400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:35:38,943 - INFO - Head 41.444\tMid 29.429\tTail 11.552\u001b[0m\n",
      "\u001b[32m2024-10-06 15:35:38,944 - INFO - epoch:  85 | train loss: 4.2490 | train accuracy: 99.392 | test loss: 3.5079 | test accuracy: 28.570 | epoch runtime:   5.49 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:35:44,334 - INFO - Evaluate Summary Time 1.72s\tLoss 3.5335\t Acc@1 28.1900\t Acc@5 50.9000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:35:44,334 - INFO - Head 41.417\tMid 29.000\tTail 10.793\u001b[0m\n",
      "\u001b[32m2024-10-06 15:35:44,334 - INFO - epoch:  86 | train loss: 4.2472 | train accuracy: 99.397 | test loss: 3.5335 | test accuracy: 28.190 | epoch runtime:   5.39 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:35:49,745 - INFO - Evaluate Summary Time 1.67s\tLoss 3.4997\t Acc@1 28.5400\t Acc@5 51.0500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:35:49,746 - INFO - Head 41.167\tMid 29.714\tTail 11.448\u001b[0m\n",
      "\u001b[32m2024-10-06 15:35:49,746 - INFO - epoch:  87 | train loss: 4.2463 | train accuracy: 99.474 | test loss: 3.4997 | test accuracy: 28.540 | epoch runtime:   5.41 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:35:55,240 - INFO - Evaluate Summary Time 1.79s\tLoss 3.5341\t Acc@1 28.5100\t Acc@5 51.2800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:35:55,241 - INFO - Head 42.028\tMid 29.543\tTail 10.483\u001b[0m\n",
      "\u001b[32m2024-10-06 15:35:55,241 - INFO - epoch:  88 | train loss: 4.2440 | train accuracy: 99.489 | test loss: 3.5341 | test accuracy: 28.510 | epoch runtime:   5.49 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:36:00,682 - INFO - Evaluate Summary Time 1.79s\tLoss 3.5136\t Acc@1 28.4300\t Acc@5 51.2000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:36:00,682 - INFO - Head 41.444\tMid 29.686\tTail 10.759\u001b[0m\n",
      "\u001b[32m2024-10-06 15:36:00,682 - INFO - epoch:  89 | train loss: 4.2433 | train accuracy: 99.510 | test loss: 3.5136 | test accuracy: 28.430 | epoch runtime:   5.44 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:36:06,205 - INFO - Evaluate Summary Time 1.77s\tLoss 3.5171\t Acc@1 28.6700\t Acc@5 51.0300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:36:06,205 - INFO - Head 42.361\tMid 28.914\tTail 11.379\u001b[0m\n",
      "\u001b[32m2024-10-06 15:36:06,206 - INFO - epoch:  90 | train loss: 4.2416 | train accuracy: 99.581 | test loss: 3.5171 | test accuracy: 28.670 | epoch runtime:   5.52 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:36:11,603 - INFO - Evaluate Summary Time 1.71s\tLoss 3.5234\t Acc@1 28.5200\t Acc@5 50.7500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:36:11,604 - INFO - Head 41.972\tMid 28.914\tTail 11.345\u001b[0m\n",
      "\u001b[32m2024-10-06 15:36:11,604 - INFO - epoch:  91 | train loss: 4.2420 | train accuracy: 99.612 | test loss: 3.5234 | test accuracy: 28.520 | epoch runtime:   5.40 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:36:17,048 - INFO - Evaluate Summary Time 1.69s\tLoss 3.4983\t Acc@1 28.6400\t Acc@5 50.8700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:36:17,048 - INFO - Head 41.917\tMid 29.429\tTail 11.207\u001b[0m\n",
      "\u001b[32m2024-10-06 15:36:17,049 - INFO - epoch:  92 | train loss: 4.2406 | train accuracy: 99.627 | test loss: 3.4983 | test accuracy: 28.640 | epoch runtime:   5.44 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:36:22,331 - INFO - Evaluate Summary Time 1.63s\tLoss 3.5230\t Acc@1 28.1800\t Acc@5 50.9600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:36:22,332 - INFO - Head 41.472\tMid 28.600\tTail 11.172\u001b[0m\n",
      "\u001b[32m2024-10-06 15:36:22,332 - INFO - epoch:  93 | train loss: 4.2380 | train accuracy: 99.566 | test loss: 3.5230 | test accuracy: 28.180 | epoch runtime:   5.28 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:36:27,855 - INFO - Evaluate Summary Time 1.75s\tLoss 3.5287\t Acc@1 28.3300\t Acc@5 50.6100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:36:27,856 - INFO - Head 41.639\tMid 28.629\tTail 11.448\u001b[0m\n",
      "\u001b[32m2024-10-06 15:36:27,856 - INFO - epoch:  94 | train loss: 4.2371 | train accuracy: 99.653 | test loss: 3.5287 | test accuracy: 28.330 | epoch runtime:   5.52 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:36:33,403 - INFO - Evaluate Summary Time 1.87s\tLoss 3.5161\t Acc@1 28.3600\t Acc@5 51.0900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:36:33,403 - INFO - Head 41.306\tMid 28.771\tTail 11.793\u001b[0m\n",
      "\u001b[32m2024-10-06 15:36:33,404 - INFO - epoch:  95 | train loss: 4.2334 | train accuracy: 99.683 | test loss: 3.5161 | test accuracy: 28.360 | epoch runtime:   5.55 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:36:38,827 - INFO - Evaluate Summary Time 1.70s\tLoss 3.5038\t Acc@1 28.4700\t Acc@5 50.9100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:36:38,827 - INFO - Head 41.722\tMid 28.629\tTail 11.828\u001b[0m\n",
      "\u001b[32m2024-10-06 15:36:38,827 - INFO - epoch:  96 | train loss: 4.2321 | train accuracy: 99.653 | test loss: 3.5038 | test accuracy: 28.470 | epoch runtime:   5.42 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:36:44,163 - INFO - Evaluate Summary Time 1.73s\tLoss 3.5259\t Acc@1 28.5200\t Acc@5 51.0400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:36:44,164 - INFO - Head 41.778\tMid 29.371\tTail 11.034\u001b[0m\n",
      "\u001b[32m2024-10-06 15:36:44,164 - INFO - epoch:  97 | train loss: 4.2330 | train accuracy: 99.724 | test loss: 3.5259 | test accuracy: 28.520 | epoch runtime:   5.34 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:36:49,520 - INFO - Evaluate Summary Time 1.66s\tLoss 3.5095\t Acc@1 28.5900\t Acc@5 51.2000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:36:49,521 - INFO - Head 42.556\tMid 28.543\tTail 11.310\u001b[0m\n",
      "\u001b[32m2024-10-06 15:36:49,521 - INFO - epoch:  98 | train loss: 4.2329 | train accuracy: 99.678 | test loss: 3.5095 | test accuracy: 28.590 | epoch runtime:   5.36 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:36:54,916 - INFO - Evaluate Summary Time 1.70s\tLoss 3.5388\t Acc@1 28.5300\t Acc@5 50.9700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:36:54,916 - INFO - Head 42.528\tMid 28.771\tTail 10.862\u001b[0m\n",
      "\u001b[32m2024-10-06 15:36:54,916 - INFO - epoch:  99 | train loss: 4.2304 | train accuracy: 99.785 | test loss: 3.5388 | test accuracy: 28.530 | epoch runtime:   5.40 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:37:00,385 - INFO - Evaluate Summary Time 1.75s\tLoss 3.5147\t Acc@1 28.5700\t Acc@5 50.8500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:37:00,386 - INFO - Head 41.944\tMid 29.114\tTail 11.310\u001b[0m\n",
      "\u001b[32m2024-10-06 15:37:00,386 - INFO - epoch: 100 | train loss: 4.2317 | train accuracy: 99.760 | test loss: 3.5147 | test accuracy: 28.570 | epoch runtime:   5.47 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:37:05,797 - INFO - Evaluate Summary Time 1.73s\tLoss 3.5180\t Acc@1 28.6400\t Acc@5 51.1000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:37:05,797 - INFO - Head 41.917\tMid 29.171\tTail 11.517\u001b[0m\n",
      "\u001b[32m2024-10-06 15:37:05,797 - INFO - epoch: 101 | train loss: 4.2304 | train accuracy: 99.755 | test loss: 3.5180 | test accuracy: 28.640 | epoch runtime:   5.41 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:37:11,169 - INFO - Evaluate Summary Time 1.75s\tLoss 3.5203\t Acc@1 28.1200\t Acc@5 50.6700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:37:11,170 - INFO - Head 41.333\tMid 28.400\tTail 11.379\u001b[0m\n",
      "\u001b[32m2024-10-06 15:37:11,170 - INFO - epoch: 102 | train loss: 4.2268 | train accuracy: 99.724 | test loss: 3.5203 | test accuracy: 28.120 | epoch runtime:   5.37 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:37:16,673 - INFO - Evaluate Summary Time 1.69s\tLoss 3.5199\t Acc@1 28.5900\t Acc@5 50.6200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:37:16,673 - INFO - Head 41.333\tMid 29.800\tTail 11.310\u001b[0m\n",
      "\u001b[32m2024-10-06 15:37:16,674 - INFO - epoch: 103 | train loss: 4.2267 | train accuracy: 99.826 | test loss: 3.5199 | test accuracy: 28.590 | epoch runtime:   5.50 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:37:22,347 - INFO - Evaluate Summary Time 1.71s\tLoss 3.5036\t Acc@1 28.4600\t Acc@5 50.9600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:37:22,347 - INFO - Head 40.778\tMid 29.914\tTail 11.414\u001b[0m\n",
      "\u001b[32m2024-10-06 15:37:22,347 - INFO - epoch: 104 | train loss: 4.2263 | train accuracy: 99.755 | test loss: 3.5036 | test accuracy: 28.460 | epoch runtime:   5.67 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:37:27,992 - INFO - Evaluate Summary Time 1.84s\tLoss 3.5214\t Acc@1 28.1300\t Acc@5 50.6200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:37:27,992 - INFO - Head 41.139\tMid 28.971\tTail 10.966\u001b[0m\n",
      "\u001b[32m2024-10-06 15:37:27,993 - INFO - epoch: 105 | train loss: 4.2248 | train accuracy: 99.801 | test loss: 3.5214 | test accuracy: 28.130 | epoch runtime:   5.64 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:37:33,402 - INFO - Evaluate Summary Time 1.78s\tLoss 3.5206\t Acc@1 28.5900\t Acc@5 50.6700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:37:33,402 - INFO - Head 40.917\tMid 29.800\tTail 11.828\u001b[0m\n",
      "\u001b[32m2024-10-06 15:37:33,402 - INFO - epoch: 106 | train loss: 4.2240 | train accuracy: 99.826 | test loss: 3.5206 | test accuracy: 28.590 | epoch runtime:   5.41 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:37:38,857 - INFO - Evaluate Summary Time 1.70s\tLoss 3.5175\t Acc@1 28.4400\t Acc@5 50.8700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:37:38,858 - INFO - Head 41.250\tMid 29.200\tTail 11.621\u001b[0m\n",
      "\u001b[32m2024-10-06 15:37:38,858 - INFO - epoch: 107 | train loss: 4.2239 | train accuracy: 99.801 | test loss: 3.5175 | test accuracy: 28.440 | epoch runtime:   5.46 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:37:44,251 - INFO - Evaluate Summary Time 1.72s\tLoss 3.5262\t Acc@1 28.4200\t Acc@5 50.7100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:37:44,251 - INFO - Head 41.389\tMid 28.971\tTail 11.655\u001b[0m\n",
      "\u001b[32m2024-10-06 15:37:44,251 - INFO - epoch: 108 | train loss: 4.2229 | train accuracy: 99.791 | test loss: 3.5262 | test accuracy: 28.420 | epoch runtime:   5.39 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:37:49,594 - INFO - Evaluate Summary Time 1.65s\tLoss 3.5250\t Acc@1 28.5300\t Acc@5 50.9300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:37:49,594 - INFO - Head 41.500\tMid 29.514\tTail 11.241\u001b[0m\n",
      "\u001b[32m2024-10-06 15:37:49,595 - INFO - epoch: 109 | train loss: 4.2204 | train accuracy: 99.811 | test loss: 3.5250 | test accuracy: 28.530 | epoch runtime:   5.34 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:37:55,006 - INFO - Evaluate Summary Time 1.66s\tLoss 3.5322\t Acc@1 28.5000\t Acc@5 50.3300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:37:55,006 - INFO - Head 41.778\tMid 29.257\tTail 11.103\u001b[0m\n",
      "\u001b[32m2024-10-06 15:37:55,007 - INFO - epoch: 110 | train loss: 4.2208 | train accuracy: 99.877 | test loss: 3.5322 | test accuracy: 28.500 | epoch runtime:   5.41 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:38:00,539 - INFO - Evaluate Summary Time 1.80s\tLoss 3.5197\t Acc@1 28.2900\t Acc@5 50.6900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:38:00,540 - INFO - Head 41.222\tMid 28.743\tTail 11.690\u001b[0m\n",
      "\u001b[32m2024-10-06 15:38:00,540 - INFO - epoch: 111 | train loss: 4.2191 | train accuracy: 99.867 | test loss: 3.5197 | test accuracy: 28.290 | epoch runtime:   5.53 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:38:06,018 - INFO - Evaluate Summary Time 1.72s\tLoss 3.5224\t Acc@1 28.4200\t Acc@5 50.4100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:38:06,019 - INFO - Head 41.389\tMid 29.086\tTail 11.517\u001b[0m\n",
      "\u001b[32m2024-10-06 15:38:06,019 - INFO - epoch: 112 | train loss: 4.2188 | train accuracy: 99.842 | test loss: 3.5224 | test accuracy: 28.420 | epoch runtime:   5.48 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:38:11,572 - INFO - Evaluate Summary Time 1.81s\tLoss 3.5167\t Acc@1 28.3200\t Acc@5 50.6800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:38:11,572 - INFO - Head 41.444\tMid 28.971\tTail 11.241\u001b[0m\n",
      "\u001b[32m2024-10-06 15:38:11,573 - INFO - epoch: 113 | train loss: 4.2171 | train accuracy: 99.857 | test loss: 3.5167 | test accuracy: 28.320 | epoch runtime:   5.55 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:38:16,989 - INFO - Evaluate Summary Time 1.68s\tLoss 3.5367\t Acc@1 28.0600\t Acc@5 50.4200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:38:16,989 - INFO - Head 41.417\tMid 28.343\tTail 11.138\u001b[0m\n",
      "\u001b[32m2024-10-06 15:38:16,989 - INFO - epoch: 114 | train loss: 4.2183 | train accuracy: 99.872 | test loss: 3.5367 | test accuracy: 28.060 | epoch runtime:   5.42 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:38:22,227 - INFO - Evaluate Summary Time 1.64s\tLoss 3.5208\t Acc@1 28.4900\t Acc@5 50.6400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:38:22,228 - INFO - Head 41.611\tMid 28.914\tTail 11.690\u001b[0m\n",
      "\u001b[32m2024-10-06 15:38:22,228 - INFO - epoch: 115 | train loss: 4.2160 | train accuracy: 99.857 | test loss: 3.5208 | test accuracy: 28.490 | epoch runtime:   5.24 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:38:27,658 - INFO - Evaluate Summary Time 1.68s\tLoss 3.5216\t Acc@1 28.2900\t Acc@5 50.4700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:38:27,658 - INFO - Head 41.250\tMid 29.000\tTail 11.345\u001b[0m\n",
      "\u001b[32m2024-10-06 15:38:27,658 - INFO - epoch: 116 | train loss: 4.2155 | train accuracy: 99.842 | test loss: 3.5216 | test accuracy: 28.290 | epoch runtime:   5.43 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:38:33,041 - INFO - Evaluate Summary Time 1.68s\tLoss 3.5117\t Acc@1 28.3100\t Acc@5 50.7000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:38:33,041 - INFO - Head 40.750\tMid 29.286\tTail 11.690\u001b[0m\n",
      "\u001b[32m2024-10-06 15:38:33,041 - INFO - epoch: 117 | train loss: 4.2168 | train accuracy: 99.872 | test loss: 3.5117 | test accuracy: 28.310 | epoch runtime:   5.38 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:38:38,551 - INFO - Evaluate Summary Time 1.76s\tLoss 3.5313\t Acc@1 28.2300\t Acc@5 50.4400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:38:38,551 - INFO - Head 40.611\tMid 29.514\tTail 11.310\u001b[0m\n",
      "\u001b[32m2024-10-06 15:38:38,552 - INFO - epoch: 118 | train loss: 4.2130 | train accuracy: 99.898 | test loss: 3.5313 | test accuracy: 28.230 | epoch runtime:   5.51 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:38:43,973 - INFO - Evaluate Summary Time 1.73s\tLoss 3.5349\t Acc@1 28.2400\t Acc@5 50.5700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:38:43,974 - INFO - Head 40.556\tMid 29.057\tTail 11.966\u001b[0m\n",
      "\u001b[32m2024-10-06 15:38:43,974 - INFO - epoch: 119 | train loss: 4.2131 | train accuracy: 99.893 | test loss: 3.5349 | test accuracy: 28.240 | epoch runtime:   5.42 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:38:49,431 - INFO - Evaluate Summary Time 1.76s\tLoss 3.5173\t Acc@1 28.4900\t Acc@5 50.5900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:38:49,431 - INFO - Head 41.056\tMid 29.314\tTail 11.897\u001b[0m\n",
      "\u001b[32m2024-10-06 15:38:49,431 - INFO - epoch: 120 | train loss: 4.2156 | train accuracy: 99.882 | test loss: 3.5173 | test accuracy: 28.490 | epoch runtime:   5.46 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:38:54,786 - INFO - Evaluate Summary Time 1.72s\tLoss 3.5201\t Acc@1 28.2700\t Acc@5 50.5700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:38:54,786 - INFO - Head 41.389\tMid 28.743\tTail 11.414\u001b[0m\n",
      "\u001b[32m2024-10-06 15:38:54,786 - INFO - epoch: 121 | train loss: 4.2135 | train accuracy: 99.888 | test loss: 3.5201 | test accuracy: 28.270 | epoch runtime:   5.35 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:39:00,223 - INFO - Evaluate Summary Time 1.74s\tLoss 3.5266\t Acc@1 28.2600\t Acc@5 50.4700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:39:00,223 - INFO - Head 41.222\tMid 28.714\tTail 11.621\u001b[0m\n",
      "\u001b[32m2024-10-06 15:39:00,224 - INFO - epoch: 122 | train loss: 4.2153 | train accuracy: 99.862 | test loss: 3.5266 | test accuracy: 28.260 | epoch runtime:   5.44 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:39:05,685 - INFO - Evaluate Summary Time 1.78s\tLoss 3.5152\t Acc@1 28.2200\t Acc@5 50.6500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:39:05,685 - INFO - Head 40.556\tMid 29.257\tTail 11.655\u001b[0m\n",
      "\u001b[32m2024-10-06 15:39:05,685 - INFO - epoch: 123 | train loss: 4.2120 | train accuracy: 99.893 | test loss: 3.5152 | test accuracy: 28.220 | epoch runtime:   5.46 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:39:11,033 - INFO - Evaluate Summary Time 1.69s\tLoss 3.5121\t Acc@1 28.6000\t Acc@5 50.8800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:39:11,033 - INFO - Head 41.778\tMid 28.857\tTail 11.931\u001b[0m\n",
      "\u001b[32m2024-10-06 15:39:11,033 - INFO - epoch: 124 | train loss: 4.2117 | train accuracy: 99.893 | test loss: 3.5121 | test accuracy: 28.600 | epoch runtime:   5.35 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:39:16,459 - INFO - Evaluate Summary Time 1.72s\tLoss 3.5249\t Acc@1 28.2600\t Acc@5 50.3600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:39:16,460 - INFO - Head 40.944\tMid 28.971\tTail 11.655\u001b[0m\n",
      "\u001b[32m2024-10-06 15:39:16,460 - INFO - epoch: 125 | train loss: 4.2112 | train accuracy: 99.918 | test loss: 3.5249 | test accuracy: 28.260 | epoch runtime:   5.43 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:39:21,853 - INFO - Evaluate Summary Time 1.72s\tLoss 3.5115\t Acc@1 28.4600\t Acc@5 50.6000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:39:21,853 - INFO - Head 41.278\tMid 29.171\tTail 11.690\u001b[0m\n",
      "\u001b[32m2024-10-06 15:39:21,854 - INFO - epoch: 126 | train loss: 4.2118 | train accuracy: 99.862 | test loss: 3.5115 | test accuracy: 28.460 | epoch runtime:   5.39 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:39:27,325 - INFO - Evaluate Summary Time 1.74s\tLoss 3.5141\t Acc@1 28.4100\t Acc@5 50.4100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:39:27,326 - INFO - Head 41.028\tMid 28.829\tTail 12.241\u001b[0m\n",
      "\u001b[32m2024-10-06 15:39:27,326 - INFO - epoch: 127 | train loss: 4.2112 | train accuracy: 99.852 | test loss: 3.5141 | test accuracy: 28.410 | epoch runtime:   5.47 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:39:32,702 - INFO - Evaluate Summary Time 1.76s\tLoss 3.5242\t Acc@1 28.4900\t Acc@5 50.4400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:39:32,703 - INFO - Head 41.583\tMid 29.057\tTail 11.552\u001b[0m\n",
      "\u001b[32m2024-10-06 15:39:32,703 - INFO - epoch: 128 | train loss: 4.2101 | train accuracy: 99.882 | test loss: 3.5242 | test accuracy: 28.490 | epoch runtime:   5.38 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:39:38,139 - INFO - Evaluate Summary Time 1.66s\tLoss 3.5269\t Acc@1 28.1700\t Acc@5 50.6600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:39:38,139 - INFO - Head 41.389\tMid 28.400\tTail 11.483\u001b[0m\n",
      "\u001b[32m2024-10-06 15:39:38,139 - INFO - epoch: 129 | train loss: 4.2077 | train accuracy: 99.898 | test loss: 3.5269 | test accuracy: 28.170 | epoch runtime:   5.44 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:39:43,590 - INFO - Evaluate Summary Time 1.76s\tLoss 3.5198\t Acc@1 28.3000\t Acc@5 50.7200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:39:43,590 - INFO - Head 41.056\tMid 29.086\tTail 11.517\u001b[0m\n",
      "\u001b[32m2024-10-06 15:39:43,590 - INFO - epoch: 130 | train loss: 4.2108 | train accuracy: 99.882 | test loss: 3.5198 | test accuracy: 28.300 | epoch runtime:   5.45 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:39:49,003 - INFO - Evaluate Summary Time 1.67s\tLoss 3.5219\t Acc@1 28.2900\t Acc@5 50.6300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:39:49,004 - INFO - Head 41.194\tMid 28.771\tTail 11.690\u001b[0m\n",
      "\u001b[32m2024-10-06 15:39:49,004 - INFO - epoch: 131 | train loss: 4.2101 | train accuracy: 99.893 | test loss: 3.5219 | test accuracy: 28.290 | epoch runtime:   5.41 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:39:54,443 - INFO - Evaluate Summary Time 1.69s\tLoss 3.5205\t Acc@1 28.2900\t Acc@5 50.8100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:39:54,444 - INFO - Head 41.194\tMid 28.571\tTail 11.931\u001b[0m\n",
      "\u001b[32m2024-10-06 15:39:54,444 - INFO - epoch: 132 | train loss: 4.2082 | train accuracy: 99.903 | test loss: 3.5205 | test accuracy: 28.290 | epoch runtime:   5.44 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:39:59,814 - INFO - Evaluate Summary Time 1.72s\tLoss 3.5245\t Acc@1 28.3600\t Acc@5 50.7400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:39:59,815 - INFO - Head 41.194\tMid 29.314\tTail 11.276\u001b[0m\n",
      "\u001b[32m2024-10-06 15:39:59,815 - INFO - epoch: 133 | train loss: 4.2102 | train accuracy: 99.923 | test loss: 3.5245 | test accuracy: 28.360 | epoch runtime:   5.37 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:40:05,192 - INFO - Evaluate Summary Time 1.67s\tLoss 3.5252\t Acc@1 28.3200\t Acc@5 50.4600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:40:05,192 - INFO - Head 40.861\tMid 29.029\tTail 11.897\u001b[0m\n",
      "\u001b[32m2024-10-06 15:40:05,192 - INFO - epoch: 134 | train loss: 4.2107 | train accuracy: 99.893 | test loss: 3.5252 | test accuracy: 28.320 | epoch runtime:   5.38 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:40:10,702 - INFO - Evaluate Summary Time 1.80s\tLoss 3.5273\t Acc@1 28.5300\t Acc@5 50.6200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:40:10,702 - INFO - Head 41.306\tMid 29.229\tTail 11.828\u001b[0m\n",
      "\u001b[32m2024-10-06 15:40:10,703 - INFO - epoch: 135 | train loss: 4.2079 | train accuracy: 99.944 | test loss: 3.5273 | test accuracy: 28.530 | epoch runtime:   5.51 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:40:16,106 - INFO - Evaluate Summary Time 1.71s\tLoss 3.5216\t Acc@1 28.1800\t Acc@5 50.1100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:40:16,106 - INFO - Head 40.611\tMid 28.943\tTail 11.828\u001b[0m\n",
      "\u001b[32m2024-10-06 15:40:16,106 - INFO - epoch: 136 | train loss: 4.2077 | train accuracy: 99.928 | test loss: 3.5216 | test accuracy: 28.180 | epoch runtime:   5.40 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:40:21,493 - INFO - Evaluate Summary Time 1.67s\tLoss 3.5174\t Acc@1 28.4500\t Acc@5 50.7400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:40:21,494 - INFO - Head 40.806\tMid 29.314\tTail 12.069\u001b[0m\n",
      "\u001b[32m2024-10-06 15:40:21,494 - INFO - epoch: 137 | train loss: 4.2065 | train accuracy: 99.903 | test loss: 3.5174 | test accuracy: 28.450 | epoch runtime:   5.39 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:40:26,887 - INFO - Evaluate Summary Time 1.72s\tLoss 3.5230\t Acc@1 28.2900\t Acc@5 50.4800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:40:26,887 - INFO - Head 40.694\tMid 28.857\tTail 12.207\u001b[0m\n",
      "\u001b[32m2024-10-06 15:40:26,888 - INFO - epoch: 138 | train loss: 4.2073 | train accuracy: 99.918 | test loss: 3.5230 | test accuracy: 28.290 | epoch runtime:   5.39 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:40:32,290 - INFO - Evaluate Summary Time 1.73s\tLoss 3.5099\t Acc@1 28.4700\t Acc@5 50.6800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:40:32,290 - INFO - Head 41.222\tMid 28.829\tTail 12.207\u001b[0m\n",
      "\u001b[32m2024-10-06 15:40:32,291 - INFO - epoch: 139 | train loss: 4.2063 | train accuracy: 99.928 | test loss: 3.5099 | test accuracy: 28.470 | epoch runtime:   5.40 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:40:37,818 - INFO - Evaluate Summary Time 1.78s\tLoss 3.5307\t Acc@1 28.3600\t Acc@5 50.6100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:40:37,819 - INFO - Head 41.250\tMid 29.086\tTail 11.483\u001b[0m\n",
      "\u001b[32m2024-10-06 15:40:37,819 - INFO - epoch: 140 | train loss: 4.2086 | train accuracy: 99.918 | test loss: 3.5307 | test accuracy: 28.360 | epoch runtime:   5.53 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:40:43,278 - INFO - Evaluate Summary Time 1.74s\tLoss 3.5254\t Acc@1 28.2300\t Acc@5 50.3600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:40:43,278 - INFO - Head 40.611\tMid 28.886\tTail 12.069\u001b[0m\n",
      "\u001b[32m2024-10-06 15:40:43,278 - INFO - epoch: 141 | train loss: 4.2081 | train accuracy: 99.928 | test loss: 3.5254 | test accuracy: 28.230 | epoch runtime:   5.46 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:40:48,643 - INFO - Evaluate Summary Time 1.70s\tLoss 3.5242\t Acc@1 28.3900\t Acc@5 50.2600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:40:48,644 - INFO - Head 41.028\tMid 29.114\tTail 11.828\u001b[0m\n",
      "\u001b[32m2024-10-06 15:40:48,644 - INFO - epoch: 142 | train loss: 4.2065 | train accuracy: 99.908 | test loss: 3.5242 | test accuracy: 28.390 | epoch runtime:   5.37 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:40:54,061 - INFO - Evaluate Summary Time 1.73s\tLoss 3.5228\t Acc@1 28.3500\t Acc@5 50.5600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:40:54,062 - INFO - Head 41.083\tMid 29.171\tTail 11.552\u001b[0m\n",
      "\u001b[32m2024-10-06 15:40:54,062 - INFO - epoch: 143 | train loss: 4.2067 | train accuracy: 99.934 | test loss: 3.5228 | test accuracy: 28.350 | epoch runtime:   5.42 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:40:59,489 - INFO - Evaluate Summary Time 1.73s\tLoss 3.5190\t Acc@1 28.4100\t Acc@5 50.4600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:40:59,489 - INFO - Head 41.333\tMid 28.914\tTail 11.759\u001b[0m\n",
      "\u001b[32m2024-10-06 15:40:59,489 - INFO - epoch: 144 | train loss: 4.2063 | train accuracy: 99.908 | test loss: 3.5190 | test accuracy: 28.410 | epoch runtime:   5.43 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:41:04,813 - INFO - Evaluate Summary Time 1.73s\tLoss 3.5092\t Acc@1 28.4600\t Acc@5 50.6300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:41:04,813 - INFO - Head 41.111\tMid 29.171\tTail 11.897\u001b[0m\n",
      "\u001b[32m2024-10-06 15:41:04,814 - INFO - epoch: 145 | train loss: 4.2066 | train accuracy: 99.934 | test loss: 3.5092 | test accuracy: 28.460 | epoch runtime:   5.32 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:41:10,286 - INFO - Evaluate Summary Time 1.79s\tLoss 3.5264\t Acc@1 28.5600\t Acc@5 50.5900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:41:10,287 - INFO - Head 41.139\tMid 29.343\tTail 12.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:41:10,287 - INFO - epoch: 146 | train loss: 4.2067 | train accuracy: 99.908 | test loss: 3.5264 | test accuracy: 28.560 | epoch runtime:   5.47 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:41:15,707 - INFO - Evaluate Summary Time 1.73s\tLoss 3.5228\t Acc@1 28.3800\t Acc@5 50.3100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:41:15,708 - INFO - Head 40.750\tMid 29.343\tTail 11.862\u001b[0m\n",
      "\u001b[32m2024-10-06 15:41:15,708 - INFO - epoch: 147 | train loss: 4.2064 | train accuracy: 99.939 | test loss: 3.5228 | test accuracy: 28.380 | epoch runtime:   5.42 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:41:21,088 - INFO - Evaluate Summary Time 1.74s\tLoss 3.5170\t Acc@1 28.1800\t Acc@5 50.4300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:41:21,089 - INFO - Head 40.833\tMid 28.829\tTail 11.690\u001b[0m\n",
      "\u001b[32m2024-10-06 15:41:21,089 - INFO - epoch: 148 | train loss: 4.2074 | train accuracy: 99.872 | test loss: 3.5170 | test accuracy: 28.180 | epoch runtime:   5.38 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:41:26,502 - INFO - Evaluate Summary Time 1.70s\tLoss 3.5205\t Acc@1 28.4200\t Acc@5 50.5300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:41:26,502 - INFO - Head 41.167\tMid 28.971\tTail 11.931\u001b[0m\n",
      "\u001b[32m2024-10-06 15:41:26,503 - INFO - epoch: 149 | train loss: 4.2067 | train accuracy: 99.898 | test loss: 3.5205 | test accuracy: 28.420 | epoch runtime:   5.41 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "\u001b[32m2024-10-06 15:41:31,885 - INFO - Evaluate Summary Time 1.70s\tLoss 3.5116\t Acc@1 28.5300\t Acc@5 50.4800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:41:31,885 - INFO - Head 41.028\tMid 29.314\tTail 12.069\u001b[0m\n",
      "\u001b[32m2024-10-06 15:41:31,886 - INFO - epoch: 150 | train loss: 4.2054 | train accuracy: 99.898 | test loss: 3.5116 | test accuracy: 28.530 | epoch runtime:   5.38 sec | best accuracy: 29.110 @ epoch: 052\u001b[0m\n",
      "Runtime of this script /home/zyx/zhengjinpeng/PNP/cifar.py : 816.0 seconds (0.227 hours)\n",
      "Config:\n",
      "{\n",
      "    database: Datasets\n",
      "    dataset: cifar100\n",
      "    n_classes: 100\n",
      "    rescale_size: 32\n",
      "    crop_size: 32\n",
      "    cfg_file: ./config/cifar100.cfg\n",
      "    synthetic_data: cifar80no\n",
      "    noise_type: symmetric\n",
      "    closeset_ratio: 0.2\n",
      "    r_ood: 0.2\n",
      "    r_imb: 0.01\n",
      "    gpu: 0\n",
      "    net: cnn\n",
      "    batch_size: 128\n",
      "    lr: 0.001\n",
      "    lr_decay: cosine\n",
      "    weight_decay: 1e-05\n",
      "    opt: adam\n",
      "    warmup_epochs: 5\n",
      "    warmup_lr_scale: 10.0\n",
      "    epochs: 150\n",
      "    save_model: False\n",
      "    use_fp16: False\n",
      "    use_grad_accumulate: False\n",
      "    project: \n",
      "    log: PENIOC\n",
      "    epsilon: 0.5\n",
      "    temperature: 0.1\n",
      "    eta: 0.5\n",
      "    alpha: 0.0\n",
      "    beta: 1.0\n",
      "    gamma: 1.0\n",
      "    omega: 0.1\n",
      "    rho: 1.0\n",
      "    loss_func_aux: mae\n",
      "    weighting: soft\n",
      "    neg_cons: False\n",
      "    activation: tanh\n",
      "    ablation: False\n",
      "    log_freq: 1\n",
      "    asym: False\n",
      "}\n",
      "\n",
      "Available GPUs Index : 0\n",
      "using CIFAR-100...\n",
      "Built imbalanced dataset, r_imb=0.01\n",
      "Mixing in OOD noise, r_ood=0.2\n",
      "Mixing in ID noise, r_id=0.2\n",
      "using CIFAR-100...\n",
      "\u001b[32m2024-10-06 15:41:38,566 - INFO - Categories: 100, Training Samples: 10847, Testing Samples: 10000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:41:38,566 - INFO - Optimizer: adam\u001b[0m\n",
      "\u001b[32m2024-10-06 15:41:38,566 - INFO - Accumulate gradients every 1 iterations --> Acutal batch size is 128\u001b[0m\n",
      "\u001b[32m2024-10-06 15:41:42,973 - INFO - Evaluate Summary Time 1.69s\tLoss 4.8078\t Acc@1 2.6700\t Acc@5 9.8600\u001b[0m                                         \n",
      "\u001b[32m2024-10-06 15:41:42,973 - INFO - Head 7.417\tMid 0.000\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:41:42,974 - INFO - epoch:   1 | train loss: 4.7386 | train accuracy:  5.319 | test loss: 4.8078 | test accuracy:  2.670 | epoch runtime:   4.41 sec | best accuracy:  2.670 @ epoch: 001\u001b[0m\n",
      "\u001b[32m2024-10-06 15:41:46,642 - INFO - Evaluate Summary Time 1.77s\tLoss 4.5039\t Acc@1 4.9100\t Acc@5 15.6100\u001b[0m                                        \n",
      "\u001b[32m2024-10-06 15:41:46,642 - INFO - Head 13.639\tMid 0.000\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:41:46,642 - INFO - epoch:   2 | train loss: 4.5382 | train accuracy:  9.357 | test loss: 4.5039 | test accuracy:  4.910 | epoch runtime:   3.67 sec | best accuracy:  4.910 @ epoch: 002\u001b[0m\n",
      "\u001b[32m2024-10-06 15:41:50,136 - INFO - Evaluate Summary Time 1.64s\tLoss 4.4039\t Acc@1 6.9500\t Acc@5 17.6600\u001b[0m                                        \n",
      "\u001b[32m2024-10-06 15:41:50,137 - INFO - Head 19.167\tMid 0.143\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:41:50,137 - INFO - epoch:   3 | train loss: 4.4578 | train accuracy: 11.976 | test loss: 4.4039 | test accuracy:  6.950 | epoch runtime:   3.49 sec | best accuracy:  6.950 @ epoch: 003\u001b[0m\n",
      "\u001b[32m2024-10-06 15:41:53,769 - INFO - Evaluate Summary Time 1.78s\tLoss 4.3687\t Acc@1 7.5500\t Acc@5 18.7800\u001b[0m                                        \n",
      "\u001b[32m2024-10-06 15:41:53,770 - INFO - Head 20.167\tMid 0.829\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:41:53,770 - INFO - epoch:   4 | train loss: 4.4088 | train accuracy: 14.345 | test loss: 4.3687 | test accuracy:  7.550 | epoch runtime:   3.63 sec | best accuracy:  7.550 @ epoch: 004\u001b[0m\n",
      "\u001b[32m2024-10-06 15:41:57,271 - INFO - Evaluate Summary Time 1.63s\tLoss 4.3002\t Acc@1 9.0800\t Acc@5 23.0400\u001b[0m                                        \n",
      "\u001b[32m2024-10-06 15:41:57,272 - INFO - Head 24.139\tMid 1.114\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:41:57,272 - INFO - epoch:   5 | train loss: 4.3770 | train accuracy: 16.014 | test loss: 4.3002 | test accuracy:  9.080 | epoch runtime:   3.50 sec | best accuracy:  9.080 @ epoch: 005\u001b[0m\n",
      "\u001b[32m2024-10-06 15:42:01,077 - INFO - Evaluate Summary Time 1.57s\tLoss 4.3001\t Acc@1 9.8100\t Acc@5 24.5000\u001b[0m                                        \n",
      "\u001b[32m2024-10-06 15:42:01,077 - INFO - Head 26.083\tMid 1.200\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:42:01,078 - INFO - epoch:   6 | train loss: 4.9746 | train accuracy: 18.540 | test loss: 4.3001 | test accuracy:  9.810 | epoch runtime:   3.81 sec | best accuracy:  9.810 @ epoch: 006\u001b[0m\n",
      "\u001b[32m2024-10-06 15:42:04,984 - INFO - Evaluate Summary Time 1.71s\tLoss 4.3031\t Acc@1 9.9100\t Acc@5 24.9500\u001b[0m                                        \n",
      "\u001b[32m2024-10-06 15:42:04,985 - INFO - Head 26.444\tMid 1.114\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:42:04,985 - INFO - epoch:   7 | train loss: 4.9293 | train accuracy: 19.333 | test loss: 4.3031 | test accuracy:  9.910 | epoch runtime:   3.91 sec | best accuracy:  9.910 @ epoch: 007\u001b[0m\n",
      "\u001b[32m2024-10-06 15:42:08,886 - INFO - Evaluate Summary Time 1.72s\tLoss 4.2885\t Acc@1 10.2100\t Acc@5 25.5200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:42:08,887 - INFO - Head 27.167\tMid 1.229\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:42:08,887 - INFO - epoch:   8 | train loss: 4.9177 | train accuracy: 19.581 | test loss: 4.2885 | test accuracy: 10.210 | epoch runtime:   3.90 sec | best accuracy: 10.210 @ epoch: 008\u001b[0m\n",
      "\u001b[32m2024-10-06 15:42:12,803 - INFO - Evaluate Summary Time 1.73s\tLoss 4.2782\t Acc@1 10.3900\t Acc@5 25.9000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:42:12,804 - INFO - Head 27.611\tMid 1.286\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:42:12,804 - INFO - epoch:   9 | train loss: 4.9090 | train accuracy: 20.301 | test loss: 4.2782 | test accuracy: 10.390 | epoch runtime:   3.92 sec | best accuracy: 10.390 @ epoch: 009\u001b[0m\n",
      "\u001b[32m2024-10-06 15:42:16,825 - INFO - Evaluate Summary Time 1.86s\tLoss 4.2579\t Acc@1 10.7100\t Acc@5 26.5500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:42:16,825 - INFO - Head 28.222\tMid 1.571\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:42:16,825 - INFO - epoch:  10 | train loss: 4.8989 | train accuracy: 20.540 | test loss: 4.2579 | test accuracy: 10.710 | epoch runtime:   4.02 sec | best accuracy: 10.710 @ epoch: 010\u001b[0m\n",
      "\u001b[32m2024-10-06 15:42:20,692 - INFO - Evaluate Summary Time 1.70s\tLoss 4.2566\t Acc@1 11.0000\t Acc@5 27.0100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:42:20,692 - INFO - Head 28.861\tMid 1.743\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:42:20,693 - INFO - epoch:  11 | train loss: 4.8928 | train accuracy: 21.167 | test loss: 4.2566 | test accuracy: 11.000 | epoch runtime:   3.87 sec | best accuracy: 11.000 @ epoch: 011\u001b[0m\n",
      "\u001b[32m2024-10-06 15:42:24,625 - INFO - Evaluate Summary Time 1.77s\tLoss 4.2403\t Acc@1 11.4400\t Acc@5 27.3600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:42:24,626 - INFO - Head 29.972\tMid 1.857\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:42:24,626 - INFO - epoch:  12 | train loss: 4.8808 | train accuracy: 21.398 | test loss: 4.2403 | test accuracy: 11.440 | epoch runtime:   3.93 sec | best accuracy: 11.440 @ epoch: 012\u001b[0m\n",
      "\u001b[32m2024-10-06 15:42:28,497 - INFO - Evaluate Summary Time 1.70s\tLoss 4.2020\t Acc@1 11.6200\t Acc@5 28.1200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:42:28,497 - INFO - Head 30.417\tMid 1.914\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:42:28,498 - INFO - epoch:  13 | train loss: 4.8711 | train accuracy: 21.951 | test loss: 4.2020 | test accuracy: 11.620 | epoch runtime:   3.87 sec | best accuracy: 11.620 @ epoch: 013\u001b[0m\n",
      "\u001b[32m2024-10-06 15:42:32,388 - INFO - Evaluate Summary Time 1.72s\tLoss 4.1835\t Acc@1 12.1100\t Acc@5 28.6700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:42:32,388 - INFO - Head 31.222\tMid 2.486\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:42:32,389 - INFO - epoch:  14 | train loss: 4.8619 | train accuracy: 22.107 | test loss: 4.1835 | test accuracy: 12.110 | epoch runtime:   3.89 sec | best accuracy: 12.110 @ epoch: 014\u001b[0m\n",
      "\u001b[32m2024-10-06 15:42:36,350 - INFO - Evaluate Summary Time 1.80s\tLoss 4.1930\t Acc@1 12.2600\t Acc@5 28.5400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:42:36,351 - INFO - Head 31.361\tMid 2.771\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:42:36,351 - INFO - epoch:  15 | train loss: 4.8587 | train accuracy: 22.974 | test loss: 4.1930 | test accuracy: 12.260 | epoch runtime:   3.96 sec | best accuracy: 12.260 @ epoch: 015\u001b[0m\n",
      "\u001b[32m2024-10-06 15:42:40,322 - INFO - Evaluate Summary Time 1.74s\tLoss 4.1764\t Acc@1 12.5600\t Acc@5 29.1600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:42:40,322 - INFO - Head 31.944\tMid 3.029\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:42:40,322 - INFO - epoch:  16 | train loss: 4.8484 | train accuracy: 23.297 | test loss: 4.1764 | test accuracy: 12.560 | epoch runtime:   3.97 sec | best accuracy: 12.560 @ epoch: 016\u001b[0m\n",
      "\u001b[32m2024-10-06 15:42:44,273 - INFO - Evaluate Summary Time 1.80s\tLoss 4.1895\t Acc@1 12.8400\t Acc@5 29.0000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:42:44,273 - INFO - Head 32.417\tMid 3.343\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:42:44,273 - INFO - epoch:  17 | train loss: 4.8307 | train accuracy: 23.859 | test loss: 4.1895 | test accuracy: 12.840 | epoch runtime:   3.95 sec | best accuracy: 12.840 @ epoch: 017\u001b[0m\n",
      "\u001b[32m2024-10-06 15:42:48,281 - INFO - Evaluate Summary Time 1.78s\tLoss 4.1537\t Acc@1 13.3200\t Acc@5 29.8900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:42:48,282 - INFO - Head 32.861\tMid 4.257\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:42:48,282 - INFO - epoch:  18 | train loss: 4.8262 | train accuracy: 24.274 | test loss: 4.1537 | test accuracy: 13.320 | epoch runtime:   4.01 sec | best accuracy: 13.320 @ epoch: 018\u001b[0m\n",
      "\u001b[32m2024-10-06 15:42:52,242 - INFO - Evaluate Summary Time 1.75s\tLoss 4.2090\t Acc@1 13.4700\t Acc@5 30.4700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:42:52,242 - INFO - Head 33.333\tMid 4.200\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:42:52,243 - INFO - epoch:  19 | train loss: 4.8047 | train accuracy: 25.067 | test loss: 4.2090 | test accuracy: 13.470 | epoch runtime:   3.96 sec | best accuracy: 13.470 @ epoch: 019\u001b[0m\n",
      "\u001b[32m2024-10-06 15:42:56,153 - INFO - Evaluate Summary Time 1.72s\tLoss 4.1594\t Acc@1 13.8400\t Acc@5 30.9500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:42:56,154 - INFO - Head 34.000\tMid 4.571\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:42:56,154 - INFO - epoch:  20 | train loss: 4.7976 | train accuracy: 26.072 | test loss: 4.1594 | test accuracy: 13.840 | epoch runtime:   3.91 sec | best accuracy: 13.840 @ epoch: 020\u001b[0m\n",
      "\u001b[32m2024-10-06 15:42:59,978 - INFO - Evaluate Summary Time 1.70s\tLoss 4.2042\t Acc@1 13.7300\t Acc@5 31.0000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:42:59,978 - INFO - Head 33.667\tMid 4.600\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:42:59,979 - INFO - epoch:  21 | train loss: 4.7749 | train accuracy: 26.809 | test loss: 4.2042 | test accuracy: 13.730 | epoch runtime:   3.82 sec | best accuracy: 13.840 @ epoch: 020\u001b[0m\n",
      "\u001b[32m2024-10-06 15:43:03,883 - INFO - Evaluate Summary Time 1.73s\tLoss 4.1941\t Acc@1 14.0600\t Acc@5 31.4000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:43:03,884 - INFO - Head 33.639\tMid 5.543\tTail 0.034\u001b[0m\n",
      "\u001b[32m2024-10-06 15:43:03,884 - INFO - epoch:  22 | train loss: 4.7623 | train accuracy: 27.667 | test loss: 4.1941 | test accuracy: 14.060 | epoch runtime:   3.91 sec | best accuracy: 14.060 @ epoch: 022\u001b[0m\n",
      "\u001b[32m2024-10-06 15:43:07,743 - INFO - Evaluate Summary Time 1.67s\tLoss 4.1856\t Acc@1 14.2800\t Acc@5 32.0500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:43:07,743 - INFO - Head 34.194\tMid 5.629\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:43:07,743 - INFO - epoch:  23 | train loss: 4.7495 | train accuracy: 28.782 | test loss: 4.1856 | test accuracy: 14.280 | epoch runtime:   3.86 sec | best accuracy: 14.280 @ epoch: 023\u001b[0m\n",
      "\u001b[32m2024-10-06 15:43:11,635 - INFO - Evaluate Summary Time 1.73s\tLoss 4.2162\t Acc@1 14.2400\t Acc@5 32.1700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:43:11,636 - INFO - Head 34.472\tMid 5.229\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:43:11,636 - INFO - epoch:  24 | train loss: 4.7417 | train accuracy: 29.741 | test loss: 4.2162 | test accuracy: 14.240 | epoch runtime:   3.89 sec | best accuracy: 14.280 @ epoch: 023\u001b[0m\n",
      "\u001b[32m2024-10-06 15:43:15,605 - INFO - Evaluate Summary Time 1.78s\tLoss 4.1593\t Acc@1 14.6600\t Acc@5 32.6900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:43:15,606 - INFO - Head 35.083\tMid 5.800\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:43:15,606 - INFO - epoch:  25 | train loss: 4.7415 | train accuracy: 31.087 | test loss: 4.1593 | test accuracy: 14.660 | epoch runtime:   3.97 sec | best accuracy: 14.660 @ epoch: 025\u001b[0m\n",
      "\u001b[32m2024-10-06 15:43:19,509 - INFO - Evaluate Summary Time 1.78s\tLoss 4.1974\t Acc@1 14.2300\t Acc@5 32.5900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:43:19,510 - INFO - Head 34.500\tMid 5.171\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:43:19,510 - INFO - epoch:  26 | train loss: 4.7219 | train accuracy: 31.714 | test loss: 4.1974 | test accuracy: 14.230 | epoch runtime:   3.90 sec | best accuracy: 14.660 @ epoch: 025\u001b[0m\n",
      "\u001b[32m2024-10-06 15:43:23,413 - INFO - Evaluate Summary Time 1.76s\tLoss 4.1779\t Acc@1 14.5500\t Acc@5 33.1500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:43:23,414 - INFO - Head 35.611\tMid 4.943\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:43:23,414 - INFO - epoch:  27 | train loss: 4.7058 | train accuracy: 32.995 | test loss: 4.1779 | test accuracy: 14.550 | epoch runtime:   3.90 sec | best accuracy: 14.660 @ epoch: 025\u001b[0m\n",
      "\u001b[32m2024-10-06 15:43:27,300 - INFO - Evaluate Summary Time 1.75s\tLoss 4.1359\t Acc@1 14.7700\t Acc@5 33.7100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:43:27,301 - INFO - Head 35.361\tMid 5.829\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:43:27,301 - INFO - epoch:  28 | train loss: 4.6958 | train accuracy: 34.240 | test loss: 4.1359 | test accuracy: 14.770 | epoch runtime:   3.89 sec | best accuracy: 14.770 @ epoch: 028\u001b[0m\n",
      "\u001b[32m2024-10-06 15:43:31,204 - INFO - Evaluate Summary Time 1.74s\tLoss 4.1251\t Acc@1 15.6200\t Acc@5 33.3000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:43:31,205 - INFO - Head 36.222\tMid 7.371\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:43:31,205 - INFO - epoch:  29 | train loss: 4.7042 | train accuracy: 34.922 | test loss: 4.1251 | test accuracy: 15.620 | epoch runtime:   3.90 sec | best accuracy: 15.620 @ epoch: 029\u001b[0m\n",
      "\u001b[32m2024-10-06 15:43:35,141 - INFO - Evaluate Summary Time 1.70s\tLoss 4.1512\t Acc@1 15.2900\t Acc@5 34.2400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:43:35,142 - INFO - Head 36.056\tMid 6.600\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:43:35,142 - INFO - epoch:  30 | train loss: 4.6900 | train accuracy: 35.918 | test loss: 4.1512 | test accuracy: 15.290 | epoch runtime:   3.94 sec | best accuracy: 15.620 @ epoch: 029\u001b[0m\n",
      "\u001b[32m2024-10-06 15:43:39,150 - INFO - Evaluate Summary Time 1.78s\tLoss 4.1544\t Acc@1 15.3000\t Acc@5 34.0900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:43:39,150 - INFO - Head 35.583\tMid 7.086\tTail 0.034\u001b[0m\n",
      "\u001b[32m2024-10-06 15:43:39,150 - INFO - epoch:  31 | train loss: 4.6712 | train accuracy: 37.503 | test loss: 4.1544 | test accuracy: 15.300 | epoch runtime:   4.01 sec | best accuracy: 15.620 @ epoch: 029\u001b[0m\n",
      "\u001b[32m2024-10-06 15:43:43,042 - INFO - Evaluate Summary Time 1.73s\tLoss 4.1377\t Acc@1 15.1500\t Acc@5 33.8000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:43:43,043 - INFO - Head 36.278\tMid 5.971\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:43:43,043 - INFO - epoch:  32 | train loss: 4.6613 | train accuracy: 38.813 | test loss: 4.1377 | test accuracy: 15.150 | epoch runtime:   3.89 sec | best accuracy: 15.620 @ epoch: 029\u001b[0m\n",
      "\u001b[32m2024-10-06 15:43:47,062 - INFO - Evaluate Summary Time 1.80s\tLoss 4.1582\t Acc@1 15.2300\t Acc@5 33.6400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:43:47,062 - INFO - Head 36.361\tMid 6.086\tTail 0.034\u001b[0m\n",
      "\u001b[32m2024-10-06 15:43:47,063 - INFO - epoch:  33 | train loss: 4.6580 | train accuracy: 39.642 | test loss: 4.1582 | test accuracy: 15.230 | epoch runtime:   4.02 sec | best accuracy: 15.620 @ epoch: 029\u001b[0m\n",
      "\u001b[32m2024-10-06 15:43:50,979 - INFO - Evaluate Summary Time 1.73s\tLoss 4.1373\t Acc@1 15.5800\t Acc@5 34.3200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:43:50,979 - INFO - Head 36.278\tMid 7.086\tTail 0.138\u001b[0m\n",
      "\u001b[32m2024-10-06 15:43:50,979 - INFO - epoch:  34 | train loss: 4.6416 | train accuracy: 41.044 | test loss: 4.1373 | test accuracy: 15.580 | epoch runtime:   3.92 sec | best accuracy: 15.620 @ epoch: 029\u001b[0m\n",
      "\u001b[32m2024-10-06 15:43:54,801 - INFO - Evaluate Summary Time 1.70s\tLoss 4.1260\t Acc@1 15.2400\t Acc@5 33.4600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:43:54,802 - INFO - Head 36.639\tMid 5.829\tTail 0.034\u001b[0m\n",
      "\u001b[32m2024-10-06 15:43:54,802 - INFO - epoch:  35 | train loss: 4.6325 | train accuracy: 42.141 | test loss: 4.1260 | test accuracy: 15.240 | epoch runtime:   3.82 sec | best accuracy: 15.620 @ epoch: 029\u001b[0m\n",
      "\u001b[32m2024-10-06 15:43:58,630 - INFO - Evaluate Summary Time 1.69s\tLoss 4.0968\t Acc@1 15.4200\t Acc@5 34.5400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:43:58,630 - INFO - Head 36.861\tMid 6.114\tTail 0.034\u001b[0m\n",
      "\u001b[32m2024-10-06 15:43:58,630 - INFO - epoch:  36 | train loss: 4.6242 | train accuracy: 43.275 | test loss: 4.0968 | test accuracy: 15.420 | epoch runtime:   3.83 sec | best accuracy: 15.620 @ epoch: 029\u001b[0m\n",
      "\u001b[32m2024-10-06 15:44:02,473 - INFO - Evaluate Summary Time 1.73s\tLoss 4.1100\t Acc@1 15.7500\t Acc@5 34.2400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:44:02,474 - INFO - Head 36.778\tMid 7.057\tTail 0.138\u001b[0m\n",
      "\u001b[32m2024-10-06 15:44:02,474 - INFO - epoch:  37 | train loss: 4.6162 | train accuracy: 45.072 | test loss: 4.1100 | test accuracy: 15.750 | epoch runtime:   3.84 sec | best accuracy: 15.750 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:44:06,430 - INFO - Evaluate Summary Time 1.82s\tLoss 4.1221\t Acc@1 15.6000\t Acc@5 34.0300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:44:06,430 - INFO - Head 36.833\tMid 6.629\tTail 0.069\u001b[0m\n",
      "\u001b[32m2024-10-06 15:44:06,430 - INFO - epoch:  38 | train loss: 4.6082 | train accuracy: 46.262 | test loss: 4.1221 | test accuracy: 15.600 | epoch runtime:   3.96 sec | best accuracy: 15.750 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:44:10,248 - INFO - Evaluate Summary Time 1.68s\tLoss 4.1247\t Acc@1 15.3400\t Acc@5 33.7100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:44:10,248 - INFO - Head 36.833\tMid 5.886\tTail 0.069\u001b[0m\n",
      "\u001b[32m2024-10-06 15:44:10,248 - INFO - epoch:  39 | train loss: 4.5947 | train accuracy: 47.552 | test loss: 4.1247 | test accuracy: 15.340 | epoch runtime:   3.82 sec | best accuracy: 15.750 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:44:14,069 - INFO - Evaluate Summary Time 1.67s\tLoss 4.1503\t Acc@1 15.0300\t Acc@5 32.7600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:44:14,070 - INFO - Head 36.389\tMid 5.457\tTail 0.069\u001b[0m\n",
      "\u001b[32m2024-10-06 15:44:14,070 - INFO - epoch:  40 | train loss: 4.5842 | train accuracy: 48.917 | test loss: 4.1503 | test accuracy: 15.030 | epoch runtime:   3.82 sec | best accuracy: 15.750 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:44:17,957 - INFO - Evaluate Summary Time 1.69s\tLoss 4.1166\t Acc@1 15.5100\t Acc@5 33.2700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:44:17,958 - INFO - Head 36.917\tMid 6.229\tTail 0.138\u001b[0m\n",
      "\u001b[32m2024-10-06 15:44:17,958 - INFO - epoch:  41 | train loss: 4.5777 | train accuracy: 50.632 | test loss: 4.1166 | test accuracy: 15.510 | epoch runtime:   3.89 sec | best accuracy: 15.750 @ epoch: 037\u001b[0m\n",
      "\u001b[32m2024-10-06 15:44:21,862 - INFO - Evaluate Summary Time 1.76s\tLoss 4.0586\t Acc@1 16.0200\t Acc@5 33.8900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:44:21,863 - INFO - Head 37.139\tMid 7.371\tTail 0.241\u001b[0m\n",
      "\u001b[32m2024-10-06 15:44:21,863 - INFO - epoch:  42 | train loss: 4.5701 | train accuracy: 51.922 | test loss: 4.0586 | test accuracy: 16.020 | epoch runtime:   3.91 sec | best accuracy: 16.020 @ epoch: 042\u001b[0m\n",
      "\u001b[32m2024-10-06 15:44:25,787 - INFO - Evaluate Summary Time 1.77s\tLoss 4.0968\t Acc@1 15.4700\t Acc@5 33.4700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:44:25,787 - INFO - Head 37.056\tMid 5.914\tTail 0.207\u001b[0m\n",
      "\u001b[32m2024-10-06 15:44:25,787 - INFO - epoch:  43 | train loss: 4.5571 | train accuracy: 53.996 | test loss: 4.0968 | test accuracy: 15.470 | epoch runtime:   3.92 sec | best accuracy: 16.020 @ epoch: 042\u001b[0m\n",
      "\u001b[32m2024-10-06 15:44:29,707 - INFO - Evaluate Summary Time 1.73s\tLoss 4.1167\t Acc@1 15.9000\t Acc@5 33.5700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:44:29,708 - INFO - Head 37.472\tMid 6.771\tTail 0.138\u001b[0m\n",
      "\u001b[32m2024-10-06 15:44:29,708 - INFO - epoch:  44 | train loss: 4.5465 | train accuracy: 55.896 | test loss: 4.1167 | test accuracy: 15.900 | epoch runtime:   3.92 sec | best accuracy: 16.020 @ epoch: 042\u001b[0m\n",
      "\u001b[32m2024-10-06 15:44:33,598 - INFO - Evaluate Summary Time 1.71s\tLoss 4.1244\t Acc@1 15.6100\t Acc@5 32.8700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:44:33,599 - INFO - Head 37.250\tMid 6.171\tTail 0.138\u001b[0m\n",
      "\u001b[32m2024-10-06 15:44:33,599 - INFO - epoch:  45 | train loss: 4.5360 | train accuracy: 57.859 | test loss: 4.1244 | test accuracy: 15.610 | epoch runtime:   3.89 sec | best accuracy: 16.020 @ epoch: 042\u001b[0m\n",
      "\u001b[32m2024-10-06 15:44:37,597 - INFO - Evaluate Summary Time 1.80s\tLoss 4.0981\t Acc@1 15.7600\t Acc@5 33.6200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:44:37,598 - INFO - Head 37.389\tMid 6.486\tTail 0.103\u001b[0m\n",
      "\u001b[32m2024-10-06 15:44:37,598 - INFO - epoch:  46 | train loss: 4.5268 | train accuracy: 59.316 | test loss: 4.0981 | test accuracy: 15.760 | epoch runtime:   4.00 sec | best accuracy: 16.020 @ epoch: 042\u001b[0m\n",
      "\u001b[32m2024-10-06 15:44:41,563 - INFO - Evaluate Summary Time 1.70s\tLoss 4.0925\t Acc@1 15.6300\t Acc@5 33.2300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:44:41,564 - INFO - Head 36.889\tMid 6.571\tTail 0.172\u001b[0m\n",
      "\u001b[32m2024-10-06 15:44:41,564 - INFO - epoch:  47 | train loss: 4.5164 | train accuracy: 61.510 | test loss: 4.0925 | test accuracy: 15.630 | epoch runtime:   3.97 sec | best accuracy: 16.020 @ epoch: 042\u001b[0m\n",
      "\u001b[32m2024-10-06 15:44:45,613 - INFO - Evaluate Summary Time 1.84s\tLoss 4.1015\t Acc@1 15.4000\t Acc@5 32.7000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:44:45,614 - INFO - Head 36.444\tMid 6.429\tTail 0.103\u001b[0m\n",
      "\u001b[32m2024-10-06 15:44:45,614 - INFO - epoch:  48 | train loss: 4.5079 | train accuracy: 63.160 | test loss: 4.1015 | test accuracy: 15.400 | epoch runtime:   4.05 sec | best accuracy: 16.020 @ epoch: 042\u001b[0m\n",
      "\u001b[32m2024-10-06 15:44:49,480 - INFO - Evaluate Summary Time 1.77s\tLoss 4.0790\t Acc@1 15.5200\t Acc@5 33.0800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:44:49,481 - INFO - Head 37.306\tMid 5.943\tTail 0.034\u001b[0m\n",
      "\u001b[32m2024-10-06 15:44:49,481 - INFO - epoch:  49 | train loss: 4.4992 | train accuracy: 65.253 | test loss: 4.0790 | test accuracy: 15.520 | epoch runtime:   3.87 sec | best accuracy: 16.020 @ epoch: 042\u001b[0m\n",
      "\u001b[32m2024-10-06 15:44:53,434 - INFO - Evaluate Summary Time 1.78s\tLoss 4.0920\t Acc@1 15.4800\t Acc@5 32.9100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:44:53,435 - INFO - Head 36.889\tMid 6.229\tTail 0.069\u001b[0m\n",
      "\u001b[32m2024-10-06 15:44:53,435 - INFO - epoch:  50 | train loss: 4.4900 | train accuracy: 66.129 | test loss: 4.0920 | test accuracy: 15.480 | epoch runtime:   3.95 sec | best accuracy: 16.020 @ epoch: 042\u001b[0m\n",
      "\u001b[32m2024-10-06 15:44:57,248 - INFO - Evaluate Summary Time 1.66s\tLoss 4.0707\t Acc@1 15.8000\t Acc@5 32.7100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:44:57,249 - INFO - Head 36.833\tMid 7.143\tTail 0.138\u001b[0m\n",
      "\u001b[32m2024-10-06 15:44:57,249 - INFO - epoch:  51 | train loss: 4.4854 | train accuracy: 67.834 | test loss: 4.0707 | test accuracy: 15.800 | epoch runtime:   3.81 sec | best accuracy: 16.020 @ epoch: 042\u001b[0m\n",
      "\u001b[32m2024-10-06 15:45:01,120 - INFO - Evaluate Summary Time 1.68s\tLoss 4.0867\t Acc@1 15.7000\t Acc@5 32.7500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:45:01,120 - INFO - Head 37.417\tMid 6.286\tTail 0.103\u001b[0m\n",
      "\u001b[32m2024-10-06 15:45:01,120 - INFO - epoch:  52 | train loss: 4.4735 | train accuracy: 69.144 | test loss: 4.0867 | test accuracy: 15.700 | epoch runtime:   3.87 sec | best accuracy: 16.020 @ epoch: 042\u001b[0m\n",
      "\u001b[32m2024-10-06 15:45:04,973 - INFO - Evaluate Summary Time 1.68s\tLoss 4.0463\t Acc@1 16.1000\t Acc@5 33.1700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:45:04,973 - INFO - Head 36.750\tMid 8.000\tTail 0.241\u001b[0m\n",
      "\u001b[32m2024-10-06 15:45:04,974 - INFO - epoch:  53 | train loss: 4.5063 | train accuracy: 70.167 | test loss: 4.0463 | test accuracy: 16.100 | epoch runtime:   3.85 sec | best accuracy: 16.100 @ epoch: 053\u001b[0m\n",
      "\u001b[32m2024-10-06 15:45:09,040 - INFO - Evaluate Summary Time 1.84s\tLoss 4.0809\t Acc@1 16.2200\t Acc@5 32.7500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:45:09,040 - INFO - Head 37.583\tMid 7.457\tTail 0.276\u001b[0m\n",
      "\u001b[32m2024-10-06 15:45:09,040 - INFO - epoch:  54 | train loss: 4.4606 | train accuracy: 73.071 | test loss: 4.0809 | test accuracy: 16.220 | epoch runtime:   4.07 sec | best accuracy: 16.220 @ epoch: 054\u001b[0m\n",
      "\u001b[32m2024-10-06 15:45:12,824 - INFO - Evaluate Summary Time 1.68s\tLoss 4.0776\t Acc@1 15.7500\t Acc@5 32.8700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:45:12,825 - INFO - Head 37.528\tMid 6.257\tTail 0.172\u001b[0m\n",
      "\u001b[32m2024-10-06 15:45:12,825 - INFO - epoch:  55 | train loss: 4.4444 | train accuracy: 75.579 | test loss: 4.0776 | test accuracy: 15.750 | epoch runtime:   3.78 sec | best accuracy: 16.220 @ epoch: 054\u001b[0m\n",
      "\u001b[32m2024-10-06 15:45:16,771 - INFO - Evaluate Summary Time 1.74s\tLoss 4.0460\t Acc@1 16.3700\t Acc@5 33.1300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:45:16,771 - INFO - Head 37.944\tMid 7.629\tTail 0.138\u001b[0m\n",
      "\u001b[32m2024-10-06 15:45:16,772 - INFO - epoch:  56 | train loss: 4.4357 | train accuracy: 77.072 | test loss: 4.0460 | test accuracy: 16.370 | epoch runtime:   3.95 sec | best accuracy: 16.370 @ epoch: 056\u001b[0m\n",
      "\u001b[32m2024-10-06 15:45:20,620 - INFO - Evaluate Summary Time 1.71s\tLoss 4.0707\t Acc@1 16.1900\t Acc@5 32.4100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:45:20,620 - INFO - Head 37.972\tMid 7.057\tTail 0.172\u001b[0m\n",
      "\u001b[32m2024-10-06 15:45:20,620 - INFO - epoch:  57 | train loss: 4.4251 | train accuracy: 79.008 | test loss: 4.0707 | test accuracy: 16.190 | epoch runtime:   3.85 sec | best accuracy: 16.370 @ epoch: 056\u001b[0m\n",
      "\u001b[32m2024-10-06 15:45:24,608 - INFO - Evaluate Summary Time 1.81s\tLoss 4.0570\t Acc@1 16.1300\t Acc@5 33.0400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:45:24,609 - INFO - Head 37.861\tMid 6.886\tTail 0.310\u001b[0m\n",
      "\u001b[32m2024-10-06 15:45:24,609 - INFO - epoch:  58 | train loss: 4.4180 | train accuracy: 80.059 | test loss: 4.0570 | test accuracy: 16.130 | epoch runtime:   3.99 sec | best accuracy: 16.370 @ epoch: 056\u001b[0m\n",
      "\u001b[32m2024-10-06 15:45:28,504 - INFO - Evaluate Summary Time 1.68s\tLoss 4.0662\t Acc@1 16.1400\t Acc@5 32.6100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:45:28,504 - INFO - Head 37.667\tMid 7.171\tTail 0.241\u001b[0m\n",
      "\u001b[32m2024-10-06 15:45:28,504 - INFO - epoch:  59 | train loss: 4.4078 | train accuracy: 82.308 | test loss: 4.0662 | test accuracy: 16.140 | epoch runtime:   3.90 sec | best accuracy: 16.370 @ epoch: 056\u001b[0m\n",
      "\u001b[32m2024-10-06 15:45:32,420 - INFO - Evaluate Summary Time 1.78s\tLoss 4.0563\t Acc@1 16.1800\t Acc@5 32.7200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:45:32,421 - INFO - Head 37.833\tMid 7.143\tTail 0.207\u001b[0m\n",
      "\u001b[32m2024-10-06 15:45:32,421 - INFO - epoch:  60 | train loss: 4.4018 | train accuracy: 83.608 | test loss: 4.0563 | test accuracy: 16.180 | epoch runtime:   3.92 sec | best accuracy: 16.370 @ epoch: 056\u001b[0m\n",
      "\u001b[32m2024-10-06 15:45:36,419 - INFO - Evaluate Summary Time 1.78s\tLoss 4.0364\t Acc@1 16.0900\t Acc@5 33.0000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:45:36,419 - INFO - Head 37.306\tMid 7.429\tTail 0.207\u001b[0m\n",
      "\u001b[32m2024-10-06 15:45:36,420 - INFO - epoch:  61 | train loss: 4.3940 | train accuracy: 84.567 | test loss: 4.0364 | test accuracy: 16.090 | epoch runtime:   4.00 sec | best accuracy: 16.370 @ epoch: 056\u001b[0m\n",
      "\u001b[32m2024-10-06 15:45:40,356 - INFO - Evaluate Summary Time 1.75s\tLoss 4.0589\t Acc@1 15.9500\t Acc@5 32.6800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:45:40,356 - INFO - Head 37.111\tMid 7.229\tTail 0.207\u001b[0m\n",
      "\u001b[32m2024-10-06 15:45:40,356 - INFO - epoch:  62 | train loss: 4.3913 | train accuracy: 85.618 | test loss: 4.0589 | test accuracy: 15.950 | epoch runtime:   3.94 sec | best accuracy: 16.370 @ epoch: 056\u001b[0m\n",
      "\u001b[32m2024-10-06 15:45:44,177 - INFO - Evaluate Summary Time 1.73s\tLoss 4.0568\t Acc@1 15.9300\t Acc@5 32.4100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:45:44,178 - INFO - Head 37.611\tMid 6.686\tTail 0.172\u001b[0m\n",
      "\u001b[32m2024-10-06 15:45:44,178 - INFO - epoch:  63 | train loss: 4.3819 | train accuracy: 87.176 | test loss: 4.0568 | test accuracy: 15.930 | epoch runtime:   3.82 sec | best accuracy: 16.370 @ epoch: 056\u001b[0m\n",
      "\u001b[32m2024-10-06 15:45:48,095 - INFO - Evaluate Summary Time 1.75s\tLoss 4.0430\t Acc@1 15.9700\t Acc@5 32.4300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:45:48,095 - INFO - Head 36.972\tMid 7.314\tTail 0.345\u001b[0m\n",
      "\u001b[32m2024-10-06 15:45:48,096 - INFO - epoch:  64 | train loss: 4.3729 | train accuracy: 87.997 | test loss: 4.0430 | test accuracy: 15.970 | epoch runtime:   3.92 sec | best accuracy: 16.370 @ epoch: 056\u001b[0m\n",
      "\u001b[32m2024-10-06 15:45:51,890 - INFO - Evaluate Summary Time 1.68s\tLoss 4.0130\t Acc@1 16.4600\t Acc@5 32.8200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:45:51,891 - INFO - Head 37.139\tMid 8.543\tTail 0.345\u001b[0m\n",
      "\u001b[32m2024-10-06 15:45:51,891 - INFO - epoch:  65 | train loss: 4.3698 | train accuracy: 89.149 | test loss: 4.0130 | test accuracy: 16.460 | epoch runtime:   3.80 sec | best accuracy: 16.460 @ epoch: 065\u001b[0m\n",
      "\u001b[32m2024-10-06 15:45:55,785 - INFO - Evaluate Summary Time 1.70s\tLoss 4.0678\t Acc@1 15.7400\t Acc@5 32.1400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:45:55,786 - INFO - Head 37.194\tMid 6.486\tTail 0.276\u001b[0m\n",
      "\u001b[32m2024-10-06 15:45:55,786 - INFO - epoch:  66 | train loss: 4.3640 | train accuracy: 90.154 | test loss: 4.0678 | test accuracy: 15.740 | epoch runtime:   3.89 sec | best accuracy: 16.460 @ epoch: 065\u001b[0m\n",
      "\u001b[32m2024-10-06 15:45:59,786 - INFO - Evaluate Summary Time 1.77s\tLoss 4.0456\t Acc@1 16.0100\t Acc@5 32.2200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:45:59,786 - INFO - Head 37.639\tMid 6.829\tTail 0.241\u001b[0m\n",
      "\u001b[32m2024-10-06 15:45:59,786 - INFO - epoch:  67 | train loss: 4.3535 | train accuracy: 91.140 | test loss: 4.0456 | test accuracy: 16.010 | epoch runtime:   4.00 sec | best accuracy: 16.460 @ epoch: 065\u001b[0m\n",
      "\u001b[32m2024-10-06 15:46:03,621 - INFO - Evaluate Summary Time 1.76s\tLoss 4.0459\t Acc@1 16.0800\t Acc@5 32.0700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:46:03,622 - INFO - Head 37.083\tMid 7.543\tTail 0.310\u001b[0m\n",
      "\u001b[32m2024-10-06 15:46:03,622 - INFO - epoch:  68 | train loss: 4.3456 | train accuracy: 92.007 | test loss: 4.0459 | test accuracy: 16.080 | epoch runtime:   3.84 sec | best accuracy: 16.460 @ epoch: 065\u001b[0m\n",
      "\u001b[32m2024-10-06 15:46:07,400 - INFO - Evaluate Summary Time 1.67s\tLoss 4.0647\t Acc@1 15.5400\t Acc@5 31.7100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:46:07,401 - INFO - Head 36.528\tMid 6.629\tTail 0.241\u001b[0m\n",
      "\u001b[32m2024-10-06 15:46:07,401 - INFO - epoch:  69 | train loss: 4.3408 | train accuracy: 92.643 | test loss: 4.0647 | test accuracy: 15.540 | epoch runtime:   3.78 sec | best accuracy: 16.460 @ epoch: 065\u001b[0m\n",
      "\u001b[32m2024-10-06 15:46:11,356 - INFO - Evaluate Summary Time 1.78s\tLoss 4.0607\t Acc@1 15.7500\t Acc@5 32.3400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:46:11,357 - INFO - Head 37.194\tMid 6.571\tTail 0.207\u001b[0m\n",
      "\u001b[32m2024-10-06 15:46:11,357 - INFO - epoch:  70 | train loss: 4.3361 | train accuracy: 93.427 | test loss: 4.0607 | test accuracy: 15.750 | epoch runtime:   3.96 sec | best accuracy: 16.460 @ epoch: 065\u001b[0m\n",
      "\u001b[32m2024-10-06 15:46:15,217 - INFO - Evaluate Summary Time 1.71s\tLoss 4.0598\t Acc@1 15.7900\t Acc@5 31.6200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:46:15,217 - INFO - Head 36.333\tMid 7.429\tTail 0.379\u001b[0m\n",
      "\u001b[32m2024-10-06 15:46:15,218 - INFO - epoch:  71 | train loss: 4.3340 | train accuracy: 94.035 | test loss: 4.0598 | test accuracy: 15.790 | epoch runtime:   3.86 sec | best accuracy: 16.460 @ epoch: 065\u001b[0m\n",
      "\u001b[32m2024-10-06 15:46:19,092 - INFO - Evaluate Summary Time 1.72s\tLoss 4.0539\t Acc@1 16.0400\t Acc@5 31.6300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:46:19,092 - INFO - Head 36.750\tMid 7.743\tTail 0.345\u001b[0m\n",
      "\u001b[32m2024-10-06 15:46:19,093 - INFO - epoch:  72 | train loss: 4.3262 | train accuracy: 94.256 | test loss: 4.0539 | test accuracy: 16.040 | epoch runtime:   3.87 sec | best accuracy: 16.460 @ epoch: 065\u001b[0m\n",
      "\u001b[32m2024-10-06 15:46:22,936 - INFO - Evaluate Summary Time 1.73s\tLoss 4.0593\t Acc@1 15.9900\t Acc@5 31.4400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:46:22,936 - INFO - Head 37.361\tMid 7.057\tTail 0.241\u001b[0m\n",
      "\u001b[32m2024-10-06 15:46:22,936 - INFO - epoch:  73 | train loss: 4.3236 | train accuracy: 94.957 | test loss: 4.0593 | test accuracy: 15.990 | epoch runtime:   3.84 sec | best accuracy: 16.460 @ epoch: 065\u001b[0m\n",
      "\u001b[32m2024-10-06 15:46:26,936 - INFO - Evaluate Summary Time 1.82s\tLoss 4.0366\t Acc@1 16.5100\t Acc@5 32.3200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:46:26,937 - INFO - Head 37.583\tMid 8.257\tTail 0.310\u001b[0m\n",
      "\u001b[32m2024-10-06 15:46:26,937 - INFO - epoch:  74 | train loss: 4.3194 | train accuracy: 95.483 | test loss: 4.0366 | test accuracy: 16.510 | epoch runtime:   4.00 sec | best accuracy: 16.510 @ epoch: 074\u001b[0m\n",
      "\u001b[32m2024-10-06 15:46:30,873 - INFO - Evaluate Summary Time 1.76s\tLoss 4.0409\t Acc@1 16.4900\t Acc@5 31.9300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:46:30,874 - INFO - Head 37.139\tMid 8.486\tTail 0.517\u001b[0m\n",
      "\u001b[32m2024-10-06 15:46:30,874 - INFO - epoch:  75 | train loss: 4.3168 | train accuracy: 95.833 | test loss: 4.0409 | test accuracy: 16.490 | epoch runtime:   3.94 sec | best accuracy: 16.510 @ epoch: 074\u001b[0m\n",
      "\u001b[32m2024-10-06 15:46:34,631 - INFO - Evaluate Summary Time 1.69s\tLoss 4.0419\t Acc@1 16.3800\t Acc@5 32.4100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:46:34,631 - INFO - Head 37.111\tMid 8.343\tTail 0.345\u001b[0m\n",
      "\u001b[32m2024-10-06 15:46:34,632 - INFO - epoch:  76 | train loss: 4.3110 | train accuracy: 96.027 | test loss: 4.0419 | test accuracy: 16.380 | epoch runtime:   3.76 sec | best accuracy: 16.510 @ epoch: 074\u001b[0m\n",
      "\u001b[32m2024-10-06 15:46:38,736 - INFO - Evaluate Summary Time 1.84s\tLoss 4.0312\t Acc@1 16.3500\t Acc@5 32.2700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:46:38,737 - INFO - Head 36.694\tMid 8.657\tTail 0.379\u001b[0m\n",
      "\u001b[32m2024-10-06 15:46:38,737 - INFO - epoch:  77 | train loss: 4.3089 | train accuracy: 96.451 | test loss: 4.0312 | test accuracy: 16.350 | epoch runtime:   4.11 sec | best accuracy: 16.510 @ epoch: 074\u001b[0m\n",
      "\u001b[32m2024-10-06 15:46:42,656 - INFO - Evaluate Summary Time 1.67s\tLoss 4.0660\t Acc@1 15.9400\t Acc@5 31.5600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:46:42,657 - INFO - Head 36.806\tMid 7.486\tTail 0.241\u001b[0m\n",
      "\u001b[32m2024-10-06 15:46:42,657 - INFO - epoch:  78 | train loss: 4.3012 | train accuracy: 96.709 | test loss: 4.0660 | test accuracy: 15.940 | epoch runtime:   3.92 sec | best accuracy: 16.510 @ epoch: 074\u001b[0m\n",
      "\u001b[32m2024-10-06 15:46:46,699 - INFO - Evaluate Summary Time 1.90s\tLoss 4.0513\t Acc@1 16.0100\t Acc@5 31.7200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:46:46,699 - INFO - Head 36.472\tMid 7.943\tTail 0.345\u001b[0m\n",
      "\u001b[32m2024-10-06 15:46:46,699 - INFO - epoch:  79 | train loss: 4.2985 | train accuracy: 97.087 | test loss: 4.0513 | test accuracy: 16.010 | epoch runtime:   4.04 sec | best accuracy: 16.510 @ epoch: 074\u001b[0m\n",
      "\u001b[32m2024-10-06 15:46:50,623 - INFO - Evaluate Summary Time 1.78s\tLoss 4.0780\t Acc@1 15.6500\t Acc@5 31.3200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:46:50,623 - INFO - Head 36.944\tMid 6.543\tTail 0.207\u001b[0m\n",
      "\u001b[32m2024-10-06 15:46:50,624 - INFO - epoch:  80 | train loss: 4.2967 | train accuracy: 97.465 | test loss: 4.0780 | test accuracy: 15.650 | epoch runtime:   3.92 sec | best accuracy: 16.510 @ epoch: 074\u001b[0m\n",
      "\u001b[32m2024-10-06 15:46:54,429 - INFO - Evaluate Summary Time 1.72s\tLoss 4.0397\t Acc@1 16.6000\t Acc@5 31.7700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:46:54,429 - INFO - Head 37.417\tMid 8.600\tTail 0.414\u001b[0m\n",
      "\u001b[32m2024-10-06 15:46:54,429 - INFO - epoch:  81 | train loss: 4.2721 | train accuracy: 98.202 | test loss: 4.0397 | test accuracy: 16.600 | epoch runtime:   3.81 sec | best accuracy: 16.600 @ epoch: 081\u001b[0m\n",
      "\u001b[32m2024-10-06 15:46:58,334 - INFO - Evaluate Summary Time 1.72s\tLoss 4.0402\t Acc@1 16.2800\t Acc@5 32.0200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:46:58,335 - INFO - Head 36.667\tMid 8.400\tTail 0.483\u001b[0m\n",
      "\u001b[32m2024-10-06 15:46:58,335 - INFO - epoch:  82 | train loss: 4.2641 | train accuracy: 98.672 | test loss: 4.0402 | test accuracy: 16.280 | epoch runtime:   3.91 sec | best accuracy: 16.600 @ epoch: 081\u001b[0m\n",
      "\u001b[32m2024-10-06 15:47:02,191 - INFO - Evaluate Summary Time 1.70s\tLoss 4.0434\t Acc@1 16.4000\t Acc@5 31.9400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:47:02,191 - INFO - Head 37.472\tMid 8.029\tTail 0.345\u001b[0m\n",
      "\u001b[32m2024-10-06 15:47:02,192 - INFO - epoch:  83 | train loss: 4.2617 | train accuracy: 98.663 | test loss: 4.0434 | test accuracy: 16.400 | epoch runtime:   3.86 sec | best accuracy: 16.600 @ epoch: 081\u001b[0m\n",
      "\u001b[32m2024-10-06 15:47:06,192 - INFO - Evaluate Summary Time 1.82s\tLoss 4.0458\t Acc@1 16.4200\t Acc@5 31.6500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:47:06,192 - INFO - Head 36.750\tMid 8.800\tTail 0.379\u001b[0m\n",
      "\u001b[32m2024-10-06 15:47:06,192 - INFO - epoch:  84 | train loss: 4.2596 | train accuracy: 98.912 | test loss: 4.0458 | test accuracy: 16.420 | epoch runtime:   4.00 sec | best accuracy: 16.600 @ epoch: 081\u001b[0m\n",
      "\u001b[32m2024-10-06 15:47:10,046 - INFO - Evaluate Summary Time 1.74s\tLoss 4.0431\t Acc@1 16.5300\t Acc@5 31.8800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:47:10,046 - INFO - Head 37.167\tMid 8.743\tTail 0.310\u001b[0m\n",
      "\u001b[32m2024-10-06 15:47:10,046 - INFO - epoch:  85 | train loss: 4.2593 | train accuracy: 98.958 | test loss: 4.0431 | test accuracy: 16.530 | epoch runtime:   3.85 sec | best accuracy: 16.600 @ epoch: 081\u001b[0m\n",
      "\u001b[32m2024-10-06 15:47:13,871 - INFO - Evaluate Summary Time 1.74s\tLoss 4.0518\t Acc@1 16.2000\t Acc@5 31.6200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:47:13,872 - INFO - Head 36.417\tMid 8.543\tTail 0.345\u001b[0m\n",
      "\u001b[32m2024-10-06 15:47:13,872 - INFO - epoch:  86 | train loss: 4.2564 | train accuracy: 98.931 | test loss: 4.0518 | test accuracy: 16.200 | epoch runtime:   3.83 sec | best accuracy: 16.600 @ epoch: 081\u001b[0m\n",
      "\u001b[32m2024-10-06 15:47:17,784 - INFO - Evaluate Summary Time 1.77s\tLoss 4.0505\t Acc@1 16.3900\t Acc@5 31.3200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:47:17,784 - INFO - Head 36.944\tMid 8.486\tTail 0.414\u001b[0m\n",
      "\u001b[32m2024-10-06 15:47:17,785 - INFO - epoch:  87 | train loss: 4.2564 | train accuracy: 99.124 | test loss: 4.0505 | test accuracy: 16.390 | epoch runtime:   3.91 sec | best accuracy: 16.600 @ epoch: 081\u001b[0m\n",
      "\u001b[32m2024-10-06 15:47:21,820 - INFO - Evaluate Summary Time 1.82s\tLoss 4.0414\t Acc@1 16.5300\t Acc@5 31.6000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:47:21,821 - INFO - Head 37.056\tMid 8.800\tTail 0.379\u001b[0m\n",
      "\u001b[32m2024-10-06 15:47:21,821 - INFO - epoch:  88 | train loss: 4.2538 | train accuracy: 99.069 | test loss: 4.0414 | test accuracy: 16.530 | epoch runtime:   4.04 sec | best accuracy: 16.600 @ epoch: 081\u001b[0m\n",
      "\u001b[32m2024-10-06 15:47:25,735 - INFO - Evaluate Summary Time 1.74s\tLoss 4.0488\t Acc@1 16.2700\t Acc@5 31.7300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:47:25,736 - INFO - Head 37.250\tMid 7.857\tTail 0.379\u001b[0m\n",
      "\u001b[32m2024-10-06 15:47:25,736 - INFO - epoch:  89 | train loss: 4.2525 | train accuracy: 99.253 | test loss: 4.0488 | test accuracy: 16.270 | epoch runtime:   3.91 sec | best accuracy: 16.600 @ epoch: 081\u001b[0m\n",
      "\u001b[32m2024-10-06 15:47:29,584 - INFO - Evaluate Summary Time 1.67s\tLoss 4.0479\t Acc@1 16.5100\t Acc@5 31.9500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:47:29,585 - INFO - Head 36.583\tMid 9.143\tTail 0.483\u001b[0m\n",
      "\u001b[32m2024-10-06 15:47:29,585 - INFO - epoch:  90 | train loss: 4.2515 | train accuracy: 99.226 | test loss: 4.0479 | test accuracy: 16.510 | epoch runtime:   3.85 sec | best accuracy: 16.600 @ epoch: 081\u001b[0m\n",
      "\u001b[32m2024-10-06 15:47:33,577 - INFO - Evaluate Summary Time 1.80s\tLoss 4.0603\t Acc@1 16.3400\t Acc@5 31.6500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:47:33,577 - INFO - Head 37.083\tMid 8.200\tTail 0.414\u001b[0m\n",
      "\u001b[32m2024-10-06 15:47:33,577 - INFO - epoch:  91 | train loss: 4.2489 | train accuracy: 99.281 | test loss: 4.0603 | test accuracy: 16.340 | epoch runtime:   3.99 sec | best accuracy: 16.600 @ epoch: 081\u001b[0m\n",
      "\u001b[32m2024-10-06 15:47:37,640 - INFO - Evaluate Summary Time 1.82s\tLoss 4.0520\t Acc@1 16.5400\t Acc@5 31.8600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:47:37,640 - INFO - Head 37.111\tMid 8.571\tTail 0.621\u001b[0m\n",
      "\u001b[32m2024-10-06 15:47:37,641 - INFO - epoch:  92 | train loss: 4.2477 | train accuracy: 99.272 | test loss: 4.0520 | test accuracy: 16.540 | epoch runtime:   4.06 sec | best accuracy: 16.600 @ epoch: 081\u001b[0m\n",
      "\u001b[32m2024-10-06 15:47:41,614 - INFO - Evaluate Summary Time 1.72s\tLoss 4.0430\t Acc@1 16.5600\t Acc@5 31.7500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:47:41,615 - INFO - Head 37.111\tMid 8.714\tTail 0.517\u001b[0m\n",
      "\u001b[32m2024-10-06 15:47:41,615 - INFO - epoch:  93 | train loss: 4.2441 | train accuracy: 99.428 | test loss: 4.0430 | test accuracy: 16.560 | epoch runtime:   3.97 sec | best accuracy: 16.600 @ epoch: 081\u001b[0m\n",
      "\u001b[32m2024-10-06 15:47:45,575 - INFO - Evaluate Summary Time 1.76s\tLoss 4.0525\t Acc@1 16.4200\t Acc@5 31.5700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:47:45,575 - INFO - Head 36.778\tMid 8.657\tTail 0.517\u001b[0m\n",
      "\u001b[32m2024-10-06 15:47:45,575 - INFO - epoch:  94 | train loss: 4.2436 | train accuracy: 99.318 | test loss: 4.0525 | test accuracy: 16.420 | epoch runtime:   3.96 sec | best accuracy: 16.600 @ epoch: 081\u001b[0m\n",
      "\u001b[32m2024-10-06 15:47:49,505 - INFO - Evaluate Summary Time 1.80s\tLoss 4.0469\t Acc@1 16.4700\t Acc@5 31.4300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:47:49,505 - INFO - Head 36.806\tMid 8.743\tTail 0.552\u001b[0m\n",
      "\u001b[32m2024-10-06 15:47:49,505 - INFO - epoch:  95 | train loss: 4.2411 | train accuracy: 99.465 | test loss: 4.0469 | test accuracy: 16.470 | epoch runtime:   3.93 sec | best accuracy: 16.600 @ epoch: 081\u001b[0m\n",
      "\u001b[32m2024-10-06 15:47:53,369 - INFO - Evaluate Summary Time 1.72s\tLoss 4.0492\t Acc@1 16.4900\t Acc@5 31.6000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:47:53,369 - INFO - Head 37.056\tMid 8.571\tTail 0.517\u001b[0m\n",
      "\u001b[32m2024-10-06 15:47:53,369 - INFO - epoch:  96 | train loss: 4.2389 | train accuracy: 99.484 | test loss: 4.0492 | test accuracy: 16.490 | epoch runtime:   3.86 sec | best accuracy: 16.600 @ epoch: 081\u001b[0m\n",
      "\u001b[32m2024-10-06 15:47:57,275 - INFO - Evaluate Summary Time 1.73s\tLoss 4.0476\t Acc@1 16.4700\t Acc@5 31.7000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:47:57,276 - INFO - Head 36.806\tMid 8.800\tTail 0.483\u001b[0m\n",
      "\u001b[32m2024-10-06 15:47:57,276 - INFO - epoch:  97 | train loss: 4.2406 | train accuracy: 99.465 | test loss: 4.0476 | test accuracy: 16.470 | epoch runtime:   3.91 sec | best accuracy: 16.600 @ epoch: 081\u001b[0m\n",
      "\u001b[32m2024-10-06 15:48:01,136 - INFO - Evaluate Summary Time 1.69s\tLoss 4.0492\t Acc@1 16.6500\t Acc@5 31.8400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:48:01,136 - INFO - Head 36.694\tMid 9.457\tTail 0.448\u001b[0m\n",
      "\u001b[32m2024-10-06 15:48:01,137 - INFO - epoch:  98 | train loss: 4.2381 | train accuracy: 99.521 | test loss: 4.0492 | test accuracy: 16.650 | epoch runtime:   3.86 sec | best accuracy: 16.650 @ epoch: 098\u001b[0m\n",
      "\u001b[32m2024-10-06 15:48:05,002 - INFO - Evaluate Summary Time 1.67s\tLoss 4.0510\t Acc@1 16.3800\t Acc@5 31.3100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:48:05,002 - INFO - Head 36.528\tMid 8.800\tTail 0.517\u001b[0m\n",
      "\u001b[32m2024-10-06 15:48:05,003 - INFO - epoch:  99 | train loss: 4.2397 | train accuracy: 99.548 | test loss: 4.0510 | test accuracy: 16.380 | epoch runtime:   3.87 sec | best accuracy: 16.650 @ epoch: 098\u001b[0m\n",
      "\u001b[32m2024-10-06 15:48:08,874 - INFO - Evaluate Summary Time 1.70s\tLoss 4.0625\t Acc@1 16.2900\t Acc@5 31.7100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:48:08,874 - INFO - Head 36.833\tMid 8.286\tTail 0.448\u001b[0m\n",
      "\u001b[32m2024-10-06 15:48:08,874 - INFO - epoch: 100 | train loss: 4.2351 | train accuracy: 99.650 | test loss: 4.0625 | test accuracy: 16.290 | epoch runtime:   3.87 sec | best accuracy: 16.650 @ epoch: 098\u001b[0m\n",
      "\u001b[32m2024-10-06 15:48:12,813 - INFO - Evaluate Summary Time 1.81s\tLoss 4.0332\t Acc@1 16.7500\t Acc@5 31.8800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:48:12,813 - INFO - Head 37.083\tMid 9.257\tTail 0.552\u001b[0m\n",
      "\u001b[32m2024-10-06 15:48:12,813 - INFO - epoch: 101 | train loss: 4.2322 | train accuracy: 99.539 | test loss: 4.0332 | test accuracy: 16.750 | epoch runtime:   3.94 sec | best accuracy: 16.750 @ epoch: 101\u001b[0m\n",
      "\u001b[32m2024-10-06 15:48:16,772 - INFO - Evaluate Summary Time 1.77s\tLoss 4.0459\t Acc@1 16.5300\t Acc@5 31.7000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:48:16,772 - INFO - Head 36.472\tMid 9.143\tTail 0.690\u001b[0m\n",
      "\u001b[32m2024-10-06 15:48:16,773 - INFO - epoch: 102 | train loss: 4.2356 | train accuracy: 99.521 | test loss: 4.0459 | test accuracy: 16.530 | epoch runtime:   3.96 sec | best accuracy: 16.750 @ epoch: 101\u001b[0m\n",
      "\u001b[32m2024-10-06 15:48:20,563 - INFO - Evaluate Summary Time 1.68s\tLoss 4.0489\t Acc@1 16.4100\t Acc@5 31.4900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:48:20,564 - INFO - Head 36.806\tMid 8.629\tTail 0.483\u001b[0m\n",
      "\u001b[32m2024-10-06 15:48:20,564 - INFO - epoch: 103 | train loss: 4.2312 | train accuracy: 99.622 | test loss: 4.0489 | test accuracy: 16.410 | epoch runtime:   3.79 sec | best accuracy: 16.750 @ epoch: 101\u001b[0m\n",
      "\u001b[32m2024-10-06 15:48:24,409 - INFO - Evaluate Summary Time 1.74s\tLoss 4.0404\t Acc@1 16.7400\t Acc@5 31.7100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:48:24,409 - INFO - Head 36.722\tMid 9.457\tTail 0.724\u001b[0m\n",
      "\u001b[32m2024-10-06 15:48:24,410 - INFO - epoch: 104 | train loss: 4.2344 | train accuracy: 99.539 | test loss: 4.0404 | test accuracy: 16.740 | epoch runtime:   3.85 sec | best accuracy: 16.750 @ epoch: 101\u001b[0m\n",
      "\u001b[32m2024-10-06 15:48:28,363 - INFO - Evaluate Summary Time 1.74s\tLoss 4.0404\t Acc@1 16.4400\t Acc@5 31.6000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:48:28,364 - INFO - Head 36.000\tMid 9.429\tTail 0.621\u001b[0m\n",
      "\u001b[32m2024-10-06 15:48:28,364 - INFO - epoch: 105 | train loss: 4.2287 | train accuracy: 99.668 | test loss: 4.0404 | test accuracy: 16.440 | epoch runtime:   3.95 sec | best accuracy: 16.750 @ epoch: 101\u001b[0m\n",
      "\u001b[32m2024-10-06 15:48:32,273 - INFO - Evaluate Summary Time 1.73s\tLoss 4.0525\t Acc@1 16.6400\t Acc@5 31.6200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:48:32,274 - INFO - Head 37.167\tMid 8.886\tTail 0.517\u001b[0m\n",
      "\u001b[32m2024-10-06 15:48:32,274 - INFO - epoch: 106 | train loss: 4.2279 | train accuracy: 99.604 | test loss: 4.0525 | test accuracy: 16.640 | epoch runtime:   3.91 sec | best accuracy: 16.750 @ epoch: 101\u001b[0m\n",
      "\u001b[32m2024-10-06 15:48:36,276 - INFO - Evaluate Summary Time 1.84s\tLoss 4.0451\t Acc@1 16.5300\t Acc@5 31.6400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:48:36,276 - INFO - Head 36.556\tMid 9.200\tTail 0.517\u001b[0m\n",
      "\u001b[32m2024-10-06 15:48:36,276 - INFO - epoch: 107 | train loss: 4.2259 | train accuracy: 99.714 | test loss: 4.0451 | test accuracy: 16.530 | epoch runtime:   4.00 sec | best accuracy: 16.750 @ epoch: 101\u001b[0m\n",
      "\u001b[32m2024-10-06 15:48:40,297 - INFO - Evaluate Summary Time 1.79s\tLoss 4.0452\t Acc@1 16.3500\t Acc@5 31.3100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:48:40,298 - INFO - Head 35.806\tMid 9.429\tTail 0.552\u001b[0m\n",
      "\u001b[32m2024-10-06 15:48:40,298 - INFO - epoch: 108 | train loss: 4.2267 | train accuracy: 99.640 | test loss: 4.0452 | test accuracy: 16.350 | epoch runtime:   4.02 sec | best accuracy: 16.750 @ epoch: 101\u001b[0m\n",
      "\u001b[32m2024-10-06 15:48:44,178 - INFO - Evaluate Summary Time 1.73s\tLoss 4.0537\t Acc@1 16.4000\t Acc@5 31.6000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:48:44,179 - INFO - Head 36.333\tMid 9.000\tTail 0.586\u001b[0m\n",
      "\u001b[32m2024-10-06 15:48:44,179 - INFO - epoch: 109 | train loss: 4.2260 | train accuracy: 99.751 | test loss: 4.0537 | test accuracy: 16.400 | epoch runtime:   3.88 sec | best accuracy: 16.750 @ epoch: 101\u001b[0m\n",
      "\u001b[32m2024-10-06 15:48:48,144 - INFO - Evaluate Summary Time 1.78s\tLoss 4.0523\t Acc@1 16.7200\t Acc@5 31.4900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:48:48,145 - INFO - Head 37.500\tMid 8.771\tTail 0.517\u001b[0m\n",
      "\u001b[32m2024-10-06 15:48:48,145 - INFO - epoch: 110 | train loss: 4.2247 | train accuracy: 99.677 | test loss: 4.0523 | test accuracy: 16.720 | epoch runtime:   3.97 sec | best accuracy: 16.750 @ epoch: 101\u001b[0m\n",
      "\u001b[32m2024-10-06 15:48:51,961 - INFO - Evaluate Summary Time 1.68s\tLoss 4.0481\t Acc@1 16.4700\t Acc@5 31.2300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:48:51,961 - INFO - Head 36.500\tMid 9.114\tTail 0.483\u001b[0m\n",
      "\u001b[32m2024-10-06 15:48:51,961 - INFO - epoch: 111 | train loss: 4.2212 | train accuracy: 99.677 | test loss: 4.0481 | test accuracy: 16.470 | epoch runtime:   3.82 sec | best accuracy: 16.750 @ epoch: 101\u001b[0m\n",
      "\u001b[32m2024-10-06 15:48:55,892 - INFO - Evaluate Summary Time 1.71s\tLoss 4.0574\t Acc@1 16.6500\t Acc@5 31.4800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:48:55,893 - INFO - Head 37.333\tMid 8.914\tTail 0.310\u001b[0m\n",
      "\u001b[32m2024-10-06 15:48:55,893 - INFO - epoch: 112 | train loss: 4.2247 | train accuracy: 99.696 | test loss: 4.0574 | test accuracy: 16.650 | epoch runtime:   3.93 sec | best accuracy: 16.750 @ epoch: 101\u001b[0m\n",
      "\u001b[32m2024-10-06 15:48:59,845 - INFO - Evaluate Summary Time 1.80s\tLoss 4.0412\t Acc@1 16.6600\t Acc@5 31.4900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:48:59,845 - INFO - Head 36.389\tMid 9.600\tTail 0.690\u001b[0m\n",
      "\u001b[32m2024-10-06 15:48:59,845 - INFO - epoch: 113 | train loss: 4.2237 | train accuracy: 99.770 | test loss: 4.0412 | test accuracy: 16.660 | epoch runtime:   3.95 sec | best accuracy: 16.750 @ epoch: 101\u001b[0m\n",
      "\u001b[32m2024-10-06 15:49:03,744 - INFO - Evaluate Summary Time 1.72s\tLoss 4.0552\t Acc@1 16.5900\t Acc@5 31.5900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:49:03,744 - INFO - Head 36.972\tMid 8.971\tTail 0.483\u001b[0m\n",
      "\u001b[32m2024-10-06 15:49:03,744 - INFO - epoch: 114 | train loss: 4.2206 | train accuracy: 99.677 | test loss: 4.0552 | test accuracy: 16.590 | epoch runtime:   3.90 sec | best accuracy: 16.750 @ epoch: 101\u001b[0m\n",
      "\u001b[32m2024-10-06 15:49:07,626 - INFO - Evaluate Summary Time 1.71s\tLoss 4.0546\t Acc@1 16.5700\t Acc@5 31.7000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:49:07,627 - INFO - Head 36.944\tMid 8.914\tTail 0.517\u001b[0m\n",
      "\u001b[32m2024-10-06 15:49:07,627 - INFO - epoch: 115 | train loss: 4.2208 | train accuracy: 99.751 | test loss: 4.0546 | test accuracy: 16.570 | epoch runtime:   3.88 sec | best accuracy: 16.750 @ epoch: 101\u001b[0m\n",
      "\u001b[32m2024-10-06 15:49:11,435 - INFO - Evaluate Summary Time 1.71s\tLoss 4.0465\t Acc@1 16.5700\t Acc@5 31.2900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:49:11,435 - INFO - Head 36.611\tMid 9.257\tTail 0.517\u001b[0m\n",
      "\u001b[32m2024-10-06 15:49:11,435 - INFO - epoch: 116 | train loss: 4.2191 | train accuracy: 99.770 | test loss: 4.0465 | test accuracy: 16.570 | epoch runtime:   3.81 sec | best accuracy: 16.750 @ epoch: 101\u001b[0m\n",
      "\u001b[32m2024-10-06 15:49:15,351 - INFO - Evaluate Summary Time 1.75s\tLoss 4.0485\t Acc@1 16.6300\t Acc@5 31.3800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:49:15,351 - INFO - Head 36.722\tMid 9.171\tTail 0.690\u001b[0m\n",
      "\u001b[32m2024-10-06 15:49:15,352 - INFO - epoch: 117 | train loss: 4.2176 | train accuracy: 99.788 | test loss: 4.0485 | test accuracy: 16.630 | epoch runtime:   3.92 sec | best accuracy: 16.750 @ epoch: 101\u001b[0m\n",
      "\u001b[32m2024-10-06 15:49:19,269 - INFO - Evaluate Summary Time 1.82s\tLoss 4.0502\t Acc@1 16.7500\t Acc@5 31.4900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:49:19,270 - INFO - Head 36.833\tMid 9.429\tTail 0.655\u001b[0m\n",
      "\u001b[32m2024-10-06 15:49:19,270 - INFO - epoch: 118 | train loss: 4.2190 | train accuracy: 99.723 | test loss: 4.0502 | test accuracy: 16.750 | epoch runtime:   3.92 sec | best accuracy: 16.750 @ epoch: 101\u001b[0m\n",
      "\u001b[32m2024-10-06 15:49:23,184 - INFO - Evaluate Summary Time 1.74s\tLoss 4.0542\t Acc@1 16.3300\t Acc@5 31.4700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:49:23,185 - INFO - Head 36.417\tMid 8.686\tTail 0.621\u001b[0m\n",
      "\u001b[32m2024-10-06 15:49:23,185 - INFO - epoch: 119 | train loss: 4.2192 | train accuracy: 99.788 | test loss: 4.0542 | test accuracy: 16.330 | epoch runtime:   3.91 sec | best accuracy: 16.750 @ epoch: 101\u001b[0m\n",
      "\u001b[32m2024-10-06 15:49:27,276 - INFO - Evaluate Summary Time 1.71s\tLoss 4.0461\t Acc@1 16.5800\t Acc@5 31.6500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:49:27,277 - INFO - Head 36.472\tMid 9.286\tTail 0.690\u001b[0m\n",
      "\u001b[32m2024-10-06 15:49:27,277 - INFO - epoch: 120 | train loss: 4.2163 | train accuracy: 99.806 | test loss: 4.0461 | test accuracy: 16.580 | epoch runtime:   4.09 sec | best accuracy: 16.750 @ epoch: 101\u001b[0m\n",
      "\u001b[32m2024-10-06 15:49:31,135 - INFO - Evaluate Summary Time 1.72s\tLoss 4.0505\t Acc@1 16.5200\t Acc@5 31.4400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:49:31,136 - INFO - Head 36.750\tMid 8.800\tTail 0.724\u001b[0m\n",
      "\u001b[32m2024-10-06 15:49:31,136 - INFO - epoch: 121 | train loss: 4.2159 | train accuracy: 99.779 | test loss: 4.0505 | test accuracy: 16.520 | epoch runtime:   3.86 sec | best accuracy: 16.750 @ epoch: 101\u001b[0m\n",
      "\u001b[32m2024-10-06 15:49:35,061 - INFO - Evaluate Summary Time 1.75s\tLoss 4.0467\t Acc@1 16.5900\t Acc@5 31.6800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:49:35,061 - INFO - Head 36.611\tMid 9.286\tTail 0.552\u001b[0m\n",
      "\u001b[32m2024-10-06 15:49:35,062 - INFO - epoch: 122 | train loss: 4.2191 | train accuracy: 99.751 | test loss: 4.0467 | test accuracy: 16.590 | epoch runtime:   3.93 sec | best accuracy: 16.750 @ epoch: 101\u001b[0m\n",
      "\u001b[32m2024-10-06 15:49:39,101 - INFO - Evaluate Summary Time 1.79s\tLoss 4.0493\t Acc@1 16.8100\t Acc@5 31.4400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:49:39,102 - INFO - Head 37.194\tMid 9.257\tTail 0.621\u001b[0m\n",
      "\u001b[32m2024-10-06 15:49:39,102 - INFO - epoch: 123 | train loss: 4.2149 | train accuracy: 99.760 | test loss: 4.0493 | test accuracy: 16.810 | epoch runtime:   4.04 sec | best accuracy: 16.810 @ epoch: 123\u001b[0m\n",
      "\u001b[32m2024-10-06 15:49:43,050 - INFO - Evaluate Summary Time 1.72s\tLoss 4.0464\t Acc@1 16.7500\t Acc@5 31.5100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:49:43,051 - INFO - Head 36.639\tMid 9.629\tTail 0.655\u001b[0m\n",
      "\u001b[32m2024-10-06 15:49:43,051 - INFO - epoch: 124 | train loss: 4.2151 | train accuracy: 99.687 | test loss: 4.0464 | test accuracy: 16.750 | epoch runtime:   3.95 sec | best accuracy: 16.810 @ epoch: 123\u001b[0m\n",
      "\u001b[32m2024-10-06 15:49:47,001 - INFO - Evaluate Summary Time 1.75s\tLoss 4.0547\t Acc@1 16.4200\t Acc@5 31.2800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:49:47,001 - INFO - Head 36.694\tMid 8.686\tTail 0.586\u001b[0m\n",
      "\u001b[32m2024-10-06 15:49:47,001 - INFO - epoch: 125 | train loss: 4.2143 | train accuracy: 99.788 | test loss: 4.0547 | test accuracy: 16.420 | epoch runtime:   3.95 sec | best accuracy: 16.810 @ epoch: 123\u001b[0m\n",
      "\u001b[32m2024-10-06 15:49:50,908 - INFO - Evaluate Summary Time 1.74s\tLoss 4.0434\t Acc@1 16.8000\t Acc@5 31.7500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:49:50,909 - INFO - Head 36.833\tMid 9.514\tTail 0.724\u001b[0m\n",
      "\u001b[32m2024-10-06 15:49:50,909 - INFO - epoch: 126 | train loss: 4.2137 | train accuracy: 99.779 | test loss: 4.0434 | test accuracy: 16.800 | epoch runtime:   3.91 sec | best accuracy: 16.810 @ epoch: 123\u001b[0m\n",
      "\u001b[32m2024-10-06 15:49:54,675 - INFO - Evaluate Summary Time 1.66s\tLoss 4.0457\t Acc@1 16.8300\t Acc@5 31.6600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:49:54,675 - INFO - Head 36.583\tMid 9.943\tTail 0.621\u001b[0m\n",
      "\u001b[32m2024-10-06 15:49:54,676 - INFO - epoch: 127 | train loss: 4.2145 | train accuracy: 99.788 | test loss: 4.0457 | test accuracy: 16.830 | epoch runtime:   3.77 sec | best accuracy: 16.830 @ epoch: 127\u001b[0m\n",
      "\u001b[32m2024-10-06 15:49:58,572 - INFO - Evaluate Summary Time 1.77s\tLoss 4.0475\t Acc@1 16.4900\t Acc@5 31.3000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:49:58,573 - INFO - Head 36.222\tMid 9.314\tTail 0.655\u001b[0m\n",
      "\u001b[32m2024-10-06 15:49:58,573 - INFO - epoch: 128 | train loss: 4.2174 | train accuracy: 99.806 | test loss: 4.0475 | test accuracy: 16.490 | epoch runtime:   3.90 sec | best accuracy: 16.830 @ epoch: 127\u001b[0m\n",
      "\u001b[32m2024-10-06 15:50:02,392 - INFO - Evaluate Summary Time 1.67s\tLoss 4.0480\t Acc@1 16.5700\t Acc@5 31.4300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:50:02,392 - INFO - Head 36.972\tMid 8.857\tTail 0.552\u001b[0m\n",
      "\u001b[32m2024-10-06 15:50:02,393 - INFO - epoch: 129 | train loss: 4.2136 | train accuracy: 99.852 | test loss: 4.0480 | test accuracy: 16.570 | epoch runtime:   3.82 sec | best accuracy: 16.830 @ epoch: 127\u001b[0m\n",
      "\u001b[32m2024-10-06 15:50:06,363 - INFO - Evaluate Summary Time 1.80s\tLoss 4.0448\t Acc@1 16.6300\t Acc@5 31.5500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:50:06,363 - INFO - Head 37.000\tMid 8.943\tTail 0.621\u001b[0m\n",
      "\u001b[32m2024-10-06 15:50:06,363 - INFO - epoch: 130 | train loss: 4.2139 | train accuracy: 99.843 | test loss: 4.0448 | test accuracy: 16.630 | epoch runtime:   3.97 sec | best accuracy: 16.830 @ epoch: 127\u001b[0m\n",
      "\u001b[32m2024-10-06 15:50:10,285 - INFO - Evaluate Summary Time 1.75s\tLoss 4.0487\t Acc@1 16.7100\t Acc@5 31.5600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:50:10,286 - INFO - Head 36.889\tMid 9.229\tTail 0.690\u001b[0m\n",
      "\u001b[32m2024-10-06 15:50:10,286 - INFO - epoch: 131 | train loss: 4.2116 | train accuracy: 99.816 | test loss: 4.0487 | test accuracy: 16.710 | epoch runtime:   3.92 sec | best accuracy: 16.830 @ epoch: 127\u001b[0m\n",
      "\u001b[32m2024-10-06 15:50:14,114 - INFO - Evaluate Summary Time 1.67s\tLoss 4.0491\t Acc@1 16.7300\t Acc@5 31.6500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:50:14,115 - INFO - Head 36.583\tMid 9.657\tTail 0.621\u001b[0m\n",
      "\u001b[32m2024-10-06 15:50:14,115 - INFO - epoch: 132 | train loss: 4.2147 | train accuracy: 99.788 | test loss: 4.0491 | test accuracy: 16.730 | epoch runtime:   3.83 sec | best accuracy: 16.830 @ epoch: 127\u001b[0m\n",
      "\u001b[32m2024-10-06 15:50:17,967 - INFO - Evaluate Summary Time 1.71s\tLoss 4.0524\t Acc@1 16.5800\t Acc@5 31.4000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:50:17,967 - INFO - Head 36.639\tMid 9.143\tTail 0.655\u001b[0m\n",
      "\u001b[32m2024-10-06 15:50:17,967 - INFO - epoch: 133 | train loss: 4.2111 | train accuracy: 99.788 | test loss: 4.0524 | test accuracy: 16.580 | epoch runtime:   3.85 sec | best accuracy: 16.830 @ epoch: 127\u001b[0m\n",
      "\u001b[32m2024-10-06 15:50:21,763 - INFO - Evaluate Summary Time 1.66s\tLoss 4.0531\t Acc@1 16.6600\t Acc@5 31.2500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:50:21,763 - INFO - Head 36.639\tMid 9.286\tTail 0.759\u001b[0m\n",
      "\u001b[32m2024-10-06 15:50:21,763 - INFO - epoch: 134 | train loss: 4.2099 | train accuracy: 99.806 | test loss: 4.0531 | test accuracy: 16.660 | epoch runtime:   3.80 sec | best accuracy: 16.830 @ epoch: 127\u001b[0m\n",
      "\u001b[32m2024-10-06 15:50:25,757 - INFO - Evaluate Summary Time 1.80s\tLoss 4.0455\t Acc@1 16.6800\t Acc@5 31.4400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:50:25,758 - INFO - Head 36.667\tMid 9.343\tTail 0.724\u001b[0m\n",
      "\u001b[32m2024-10-06 15:50:25,758 - INFO - epoch: 135 | train loss: 4.2115 | train accuracy: 99.862 | test loss: 4.0455 | test accuracy: 16.680 | epoch runtime:   3.99 sec | best accuracy: 16.830 @ epoch: 127\u001b[0m\n",
      "\u001b[32m2024-10-06 15:50:29,627 - INFO - Evaluate Summary Time 1.72s\tLoss 4.0484\t Acc@1 16.6400\t Acc@5 31.5200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:50:29,628 - INFO - Head 36.694\tMid 9.314\tTail 0.586\u001b[0m\n",
      "\u001b[32m2024-10-06 15:50:29,628 - INFO - epoch: 136 | train loss: 4.2092 | train accuracy: 99.797 | test loss: 4.0484 | test accuracy: 16.640 | epoch runtime:   3.87 sec | best accuracy: 16.830 @ epoch: 127\u001b[0m\n",
      "\u001b[32m2024-10-06 15:50:33,501 - INFO - Evaluate Summary Time 1.69s\tLoss 4.0469\t Acc@1 16.5700\t Acc@5 31.5200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:50:33,501 - INFO - Head 36.583\tMid 9.171\tTail 0.655\u001b[0m\n",
      "\u001b[32m2024-10-06 15:50:33,502 - INFO - epoch: 137 | train loss: 4.2148 | train accuracy: 99.770 | test loss: 4.0469 | test accuracy: 16.570 | epoch runtime:   3.87 sec | best accuracy: 16.830 @ epoch: 127\u001b[0m\n",
      "\u001b[32m2024-10-06 15:50:37,541 - INFO - Evaluate Summary Time 1.84s\tLoss 4.0499\t Acc@1 16.7000\t Acc@5 31.4800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:50:37,541 - INFO - Head 36.583\tMid 9.429\tTail 0.793\u001b[0m\n",
      "\u001b[32m2024-10-06 15:50:37,541 - INFO - epoch: 138 | train loss: 4.2075 | train accuracy: 99.871 | test loss: 4.0499 | test accuracy: 16.700 | epoch runtime:   4.04 sec | best accuracy: 16.830 @ epoch: 127\u001b[0m\n",
      "\u001b[32m2024-10-06 15:50:41,502 - INFO - Evaluate Summary Time 1.75s\tLoss 4.0494\t Acc@1 16.6700\t Acc@5 31.3900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:50:41,503 - INFO - Head 36.639\tMid 9.371\tTail 0.690\u001b[0m\n",
      "\u001b[32m2024-10-06 15:50:41,503 - INFO - epoch: 139 | train loss: 4.2101 | train accuracy: 99.862 | test loss: 4.0494 | test accuracy: 16.670 | epoch runtime:   3.96 sec | best accuracy: 16.830 @ epoch: 127\u001b[0m\n",
      "\u001b[32m2024-10-06 15:50:45,514 - INFO - Evaluate Summary Time 1.80s\tLoss 4.0546\t Acc@1 16.3100\t Acc@5 31.2000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:50:45,515 - INFO - Head 35.972\tMid 9.029\tTail 0.690\u001b[0m\n",
      "\u001b[32m2024-10-06 15:50:45,515 - INFO - epoch: 140 | train loss: 4.2095 | train accuracy: 99.788 | test loss: 4.0546 | test accuracy: 16.310 | epoch runtime:   4.01 sec | best accuracy: 16.830 @ epoch: 127\u001b[0m\n",
      "\u001b[32m2024-10-06 15:50:49,424 - INFO - Evaluate Summary Time 1.76s\tLoss 4.0478\t Acc@1 16.4700\t Acc@5 31.6300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:50:49,424 - INFO - Head 36.194\tMid 9.314\tTail 0.621\u001b[0m\n",
      "\u001b[32m2024-10-06 15:50:49,425 - INFO - epoch: 141 | train loss: 4.2094 | train accuracy: 99.797 | test loss: 4.0478 | test accuracy: 16.470 | epoch runtime:   3.91 sec | best accuracy: 16.830 @ epoch: 127\u001b[0m\n",
      "\u001b[32m2024-10-06 15:50:53,324 - INFO - Evaluate Summary Time 1.74s\tLoss 4.0463\t Acc@1 16.7500\t Acc@5 31.7300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:50:53,324 - INFO - Head 36.833\tMid 9.371\tTail 0.724\u001b[0m\n",
      "\u001b[32m2024-10-06 15:50:53,324 - INFO - epoch: 142 | train loss: 4.2100 | train accuracy: 99.770 | test loss: 4.0463 | test accuracy: 16.750 | epoch runtime:   3.90 sec | best accuracy: 16.830 @ epoch: 127\u001b[0m\n",
      "\u001b[32m2024-10-06 15:50:57,101 - INFO - Evaluate Summary Time 1.63s\tLoss 4.0504\t Acc@1 16.6100\t Acc@5 31.6600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:50:57,101 - INFO - Head 36.611\tMid 9.257\tTail 0.655\u001b[0m\n",
      "\u001b[32m2024-10-06 15:50:57,102 - INFO - epoch: 143 | train loss: 4.2091 | train accuracy: 99.862 | test loss: 4.0504 | test accuracy: 16.610 | epoch runtime:   3.78 sec | best accuracy: 16.830 @ epoch: 127\u001b[0m\n",
      "\u001b[32m2024-10-06 15:51:01,033 - INFO - Evaluate Summary Time 1.78s\tLoss 4.0490\t Acc@1 16.7300\t Acc@5 31.6700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:51:01,034 - INFO - Head 37.250\tMid 8.971\tTail 0.621\u001b[0m\n",
      "\u001b[32m2024-10-06 15:51:01,034 - INFO - epoch: 144 | train loss: 4.2096 | train accuracy: 99.806 | test loss: 4.0490 | test accuracy: 16.730 | epoch runtime:   3.93 sec | best accuracy: 16.830 @ epoch: 127\u001b[0m\n",
      "\u001b[32m2024-10-06 15:51:04,939 - INFO - Evaluate Summary Time 1.77s\tLoss 4.0505\t Acc@1 16.6300\t Acc@5 31.6300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:51:04,940 - INFO - Head 36.694\tMid 9.286\tTail 0.586\u001b[0m\n",
      "\u001b[32m2024-10-06 15:51:04,940 - INFO - epoch: 145 | train loss: 4.2081 | train accuracy: 99.843 | test loss: 4.0505 | test accuracy: 16.630 | epoch runtime:   3.91 sec | best accuracy: 16.830 @ epoch: 127\u001b[0m\n",
      "\u001b[32m2024-10-06 15:51:08,781 - INFO - Evaluate Summary Time 1.72s\tLoss 4.0528\t Acc@1 16.5200\t Acc@5 31.4000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:51:08,782 - INFO - Head 36.306\tMid 9.286\tTail 0.690\u001b[0m\n",
      "\u001b[32m2024-10-06 15:51:08,782 - INFO - epoch: 146 | train loss: 4.2090 | train accuracy: 99.816 | test loss: 4.0528 | test accuracy: 16.520 | epoch runtime:   3.84 sec | best accuracy: 16.830 @ epoch: 127\u001b[0m\n",
      "\u001b[32m2024-10-06 15:51:12,660 - INFO - Evaluate Summary Time 1.72s\tLoss 4.0465\t Acc@1 16.5300\t Acc@5 31.4900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:51:12,660 - INFO - Head 36.417\tMid 9.171\tTail 0.724\u001b[0m\n",
      "\u001b[32m2024-10-06 15:51:12,661 - INFO - epoch: 147 | train loss: 4.2099 | train accuracy: 99.797 | test loss: 4.0465 | test accuracy: 16.530 | epoch runtime:   3.88 sec | best accuracy: 16.830 @ epoch: 127\u001b[0m\n",
      "\u001b[32m2024-10-06 15:51:16,530 - INFO - Evaluate Summary Time 1.71s\tLoss 4.0458\t Acc@1 16.6400\t Acc@5 31.3400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:51:16,531 - INFO - Head 36.278\tMid 9.571\tTail 0.793\u001b[0m\n",
      "\u001b[32m2024-10-06 15:51:16,531 - INFO - epoch: 148 | train loss: 4.2103 | train accuracy: 99.797 | test loss: 4.0458 | test accuracy: 16.640 | epoch runtime:   3.87 sec | best accuracy: 16.830 @ epoch: 127\u001b[0m\n",
      "\u001b[32m2024-10-06 15:51:20,388 - INFO - Evaluate Summary Time 1.72s\tLoss 4.0528\t Acc@1 16.7200\t Acc@5 31.4900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:51:20,388 - INFO - Head 36.611\tMid 9.629\tTail 0.586\u001b[0m\n",
      "\u001b[32m2024-10-06 15:51:20,389 - INFO - epoch: 149 | train loss: 4.2060 | train accuracy: 99.834 | test loss: 4.0528 | test accuracy: 16.720 | epoch runtime:   3.86 sec | best accuracy: 16.830 @ epoch: 127\u001b[0m\n",
      "\u001b[32m2024-10-06 15:51:24,325 - INFO - Evaluate Summary Time 1.78s\tLoss 4.0524\t Acc@1 16.6200\t Acc@5 31.5000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:51:24,325 - INFO - Head 36.639\tMid 9.314\tTail 0.586\u001b[0m\n",
      "\u001b[32m2024-10-06 15:51:24,326 - INFO - epoch: 150 | train loss: 4.2092 | train accuracy: 99.788 | test loss: 4.0524 | test accuracy: 16.620 | epoch runtime:   3.94 sec | best accuracy: 16.830 @ epoch: 127\u001b[0m\n",
      "Runtime of this script /home/zyx/zhengjinpeng/PNP/cifar.py : 589.6 seconds (0.164 hours)\n",
      "Config:\n",
      "{\n",
      "    database: Datasets\n",
      "    dataset: cifar100\n",
      "    n_classes: 100\n",
      "    rescale_size: 32\n",
      "    crop_size: 32\n",
      "    cfg_file: ./config/cifar100.cfg\n",
      "    synthetic_data: cifar80no\n",
      "    noise_type: asymmetric\n",
      "    closeset_ratio: 0.2\n",
      "    r_ood: 0.2\n",
      "    r_imb: 0.01\n",
      "    gpu: 0\n",
      "    net: cnn\n",
      "    batch_size: 128\n",
      "    lr: 0.001\n",
      "    lr_decay: cosine\n",
      "    weight_decay: 1e-05\n",
      "    opt: adam\n",
      "    warmup_epochs: 5\n",
      "    warmup_lr_scale: 10.0\n",
      "    epochs: 150\n",
      "    save_model: False\n",
      "    use_fp16: False\n",
      "    use_grad_accumulate: False\n",
      "    project: \n",
      "    log: PENIOC\n",
      "    epsilon: 0.5\n",
      "    temperature: 0.1\n",
      "    eta: 0.5\n",
      "    alpha: 0.0\n",
      "    beta: 1.0\n",
      "    gamma: 1.0\n",
      "    omega: 0.1\n",
      "    rho: 1.0\n",
      "    loss_func_aux: mae\n",
      "    weighting: soft\n",
      "    neg_cons: False\n",
      "    activation: tanh\n",
      "    ablation: False\n",
      "    log_freq: 1\n",
      "    asym: True\n",
      "}\n",
      "\n",
      "Available GPUs Index : 0\n",
      "using CIFAR-100...\n",
      "Built imbalanced dataset, r_imb=0.01\n",
      "Mixing in OOD noise, r_ood=0.2\n",
      "[ 0  0  0 ... 95 99 99]\n",
      "Actual noise 0.20\n",
      "Mixing in ID asym noise, r_id=0.2\n",
      "using CIFAR-100...\n",
      "\u001b[32m2024-10-06 15:51:30,892 - INFO - Categories: 100, Training Samples: 10847, Testing Samples: 10000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:51:30,892 - INFO - Optimizer: adam\u001b[0m\n",
      "\u001b[32m2024-10-06 15:51:30,892 - INFO - Accumulate gradients every 1 iterations --> Acutal batch size is 128\u001b[0m\n",
      "\u001b[32m2024-10-06 15:51:35,353 - INFO - Evaluate Summary Time 1.75s\tLoss 4.7297\t Acc@1 2.5000\t Acc@5 9.8900\u001b[0m                                         \n",
      "\u001b[32m2024-10-06 15:51:35,353 - INFO - Head 6.944\tMid 0.000\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:51:35,353 - INFO - epoch:   1 | train loss: 4.6383 | train accuracy:  6.066 | test loss: 4.7297 | test accuracy:  2.500 | epoch runtime:   4.46 sec | best accuracy:  2.500 @ epoch: 001\u001b[0m\n",
      "\u001b[32m2024-10-06 15:51:38,986 - INFO - Evaluate Summary Time 1.70s\tLoss 4.5826\t Acc@1 4.9300\t Acc@5 15.4400\u001b[0m                                        \n",
      "\u001b[32m2024-10-06 15:51:38,986 - INFO - Head 13.694\tMid 0.000\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:51:38,986 - INFO - epoch:   2 | train loss: 4.4577 | train accuracy: 10.233 | test loss: 4.5826 | test accuracy:  4.930 | epoch runtime:   3.63 sec | best accuracy:  4.930 @ epoch: 002\u001b[0m\n",
      "\u001b[32m2024-10-06 15:51:42,661 - INFO - Evaluate Summary Time 1.78s\tLoss 4.5070\t Acc@1 6.3300\t Acc@5 15.9900\u001b[0m                                        \n",
      "\u001b[32m2024-10-06 15:51:42,661 - INFO - Head 17.556\tMid 0.029\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:51:42,661 - INFO - epoch:   3 | train loss: 4.3793 | train accuracy: 13.967 | test loss: 4.5070 | test accuracy:  6.330 | epoch runtime:   3.67 sec | best accuracy:  6.330 @ epoch: 003\u001b[0m\n",
      "\u001b[32m2024-10-06 15:51:46,291 - INFO - Evaluate Summary Time 1.73s\tLoss 4.3402\t Acc@1 8.6800\t Acc@5 19.6400\u001b[0m                                        \n",
      "\u001b[32m2024-10-06 15:51:46,291 - INFO - Head 23.972\tMid 0.143\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:51:46,291 - INFO - epoch:   4 | train loss: 4.3328 | train accuracy: 16.106 | test loss: 4.3402 | test accuracy:  8.680 | epoch runtime:   3.63 sec | best accuracy:  8.680 @ epoch: 004\u001b[0m\n",
      "\u001b[32m2024-10-06 15:51:49,839 - INFO - Evaluate Summary Time 1.70s\tLoss 4.3280\t Acc@1 8.9800\t Acc@5 22.5100\u001b[0m                                        \n",
      "\u001b[32m2024-10-06 15:51:49,839 - INFO - Head 23.333\tMid 1.657\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:51:49,840 - INFO - epoch:   5 | train loss: 4.2929 | train accuracy: 18.355 | test loss: 4.3280 | test accuracy:  8.980 | epoch runtime:   3.55 sec | best accuracy:  8.980 @ epoch: 005\u001b[0m\n",
      "\u001b[32m2024-10-06 15:51:53,832 - INFO - Evaluate Summary Time 1.73s\tLoss 4.2365\t Acc@1 11.3100\t Acc@5 25.9700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:51:53,832 - INFO - Head 28.694\tMid 2.800\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:51:53,833 - INFO - epoch:   6 | train loss: 4.9512 | train accuracy: 20.789 | test loss: 4.2365 | test accuracy: 11.310 | epoch runtime:   3.99 sec | best accuracy: 11.310 @ epoch: 006\u001b[0m\n",
      "\u001b[32m2024-10-06 15:51:57,798 - INFO - Evaluate Summary Time 1.73s\tLoss 4.2196\t Acc@1 11.3100\t Acc@5 26.6500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:51:57,799 - INFO - Head 29.111\tMid 2.371\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:51:57,799 - INFO - epoch:   7 | train loss: 4.8934 | train accuracy: 22.568 | test loss: 4.2196 | test accuracy: 11.310 | epoch runtime:   3.97 sec | best accuracy: 11.310 @ epoch: 006\u001b[0m\n",
      "\u001b[32m2024-10-06 15:52:01,677 - INFO - Evaluate Summary Time 1.74s\tLoss 4.1976\t Acc@1 11.6500\t Acc@5 27.4700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:52:01,678 - INFO - Head 29.806\tMid 2.629\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:52:01,678 - INFO - epoch:   8 | train loss: 4.8796 | train accuracy: 23.002 | test loss: 4.1976 | test accuracy: 11.650 | epoch runtime:   3.88 sec | best accuracy: 11.650 @ epoch: 008\u001b[0m\n",
      "\u001b[32m2024-10-06 15:52:05,603 - INFO - Evaluate Summary Time 1.74s\tLoss 4.1973\t Acc@1 11.9400\t Acc@5 27.4500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:52:05,604 - INFO - Head 30.222\tMid 3.029\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:52:05,604 - INFO - epoch:   9 | train loss: 4.8674 | train accuracy: 23.693 | test loss: 4.1973 | test accuracy: 11.940 | epoch runtime:   3.93 sec | best accuracy: 11.940 @ epoch: 009\u001b[0m\n",
      "\u001b[32m2024-10-06 15:52:09,404 - INFO - Evaluate Summary Time 1.71s\tLoss 4.1956\t Acc@1 12.2700\t Acc@5 27.9100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:52:09,404 - INFO - Head 31.056\tMid 3.114\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:52:09,405 - INFO - epoch:  10 | train loss: 4.8614 | train accuracy: 24.025 | test loss: 4.1956 | test accuracy: 12.270 | epoch runtime:   3.80 sec | best accuracy: 12.270 @ epoch: 010\u001b[0m\n",
      "\u001b[32m2024-10-06 15:52:13,295 - INFO - Evaluate Summary Time 1.77s\tLoss 4.2148\t Acc@1 12.1300\t Acc@5 27.2500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:52:13,296 - INFO - Head 30.944\tMid 2.829\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:52:13,296 - INFO - epoch:  11 | train loss: 4.8542 | train accuracy: 24.504 | test loss: 4.2148 | test accuracy: 12.130 | epoch runtime:   3.89 sec | best accuracy: 12.270 @ epoch: 010\u001b[0m\n",
      "\u001b[32m2024-10-06 15:52:17,109 - INFO - Evaluate Summary Time 1.70s\tLoss 4.1517\t Acc@1 12.7000\t Acc@5 29.0800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:52:17,110 - INFO - Head 31.972\tMid 3.400\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:52:17,110 - INFO - epoch:  12 | train loss: 4.8491 | train accuracy: 24.753 | test loss: 4.1517 | test accuracy: 12.700 | epoch runtime:   3.81 sec | best accuracy: 12.700 @ epoch: 012\u001b[0m\n",
      "\u001b[32m2024-10-06 15:52:21,034 - INFO - Evaluate Summary Time 1.76s\tLoss 4.1365\t Acc@1 12.9200\t Acc@5 29.4800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:52:21,035 - INFO - Head 32.194\tMid 3.800\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:52:21,035 - INFO - epoch:  13 | train loss: 4.8333 | train accuracy: 25.703 | test loss: 4.1365 | test accuracy: 12.920 | epoch runtime:   3.92 sec | best accuracy: 12.920 @ epoch: 013\u001b[0m\n",
      "\u001b[32m2024-10-06 15:52:24,942 - INFO - Evaluate Summary Time 1.81s\tLoss 4.1545\t Acc@1 13.3000\t Acc@5 29.2200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:52:24,942 - INFO - Head 32.944\tMid 4.114\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:52:24,943 - INFO - epoch:  14 | train loss: 4.8141 | train accuracy: 26.081 | test loss: 4.1545 | test accuracy: 13.300 | epoch runtime:   3.91 sec | best accuracy: 13.300 @ epoch: 014\u001b[0m\n",
      "\u001b[32m2024-10-06 15:52:28,854 - INFO - Evaluate Summary Time 1.76s\tLoss 4.1305\t Acc@1 13.7200\t Acc@5 30.4300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:52:28,854 - INFO - Head 33.500\tMid 4.743\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:52:28,855 - INFO - epoch:  15 | train loss: 4.7904 | train accuracy: 26.551 | test loss: 4.1305 | test accuracy: 13.720 | epoch runtime:   3.91 sec | best accuracy: 13.720 @ epoch: 015\u001b[0m\n",
      "\u001b[32m2024-10-06 15:52:32,601 - INFO - Evaluate Summary Time 1.64s\tLoss 4.1309\t Acc@1 13.6300\t Acc@5 30.0900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:52:32,601 - INFO - Head 32.778\tMid 5.229\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:52:32,601 - INFO - epoch:  16 | train loss: 4.7795 | train accuracy: 27.519 | test loss: 4.1309 | test accuracy: 13.630 | epoch runtime:   3.75 sec | best accuracy: 13.720 @ epoch: 015\u001b[0m\n",
      "\u001b[32m2024-10-06 15:52:36,624 - INFO - Evaluate Summary Time 1.87s\tLoss 4.1236\t Acc@1 13.9600\t Acc@5 30.9800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:52:36,624 - INFO - Head 33.306\tMid 5.629\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:52:36,625 - INFO - epoch:  17 | train loss: 4.7550 | train accuracy: 28.340 | test loss: 4.1236 | test accuracy: 13.960 | epoch runtime:   4.02 sec | best accuracy: 13.960 @ epoch: 017\u001b[0m\n",
      "\u001b[32m2024-10-06 15:52:40,628 - INFO - Evaluate Summary Time 1.76s\tLoss 4.0955\t Acc@1 14.5700\t Acc@5 31.6900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:52:40,629 - INFO - Head 34.556\tMid 6.086\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:52:40,629 - INFO - epoch:  18 | train loss: 4.7389 | train accuracy: 29.280 | test loss: 4.0955 | test accuracy: 14.570 | epoch runtime:   4.00 sec | best accuracy: 14.570 @ epoch: 018\u001b[0m\n",
      "\u001b[32m2024-10-06 15:52:44,628 - INFO - Evaluate Summary Time 1.75s\tLoss 4.1251\t Acc@1 15.2300\t Acc@5 32.2900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:52:44,628 - INFO - Head 35.333\tMid 7.171\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:52:44,628 - INFO - epoch:  19 | train loss: 4.7217 | train accuracy: 30.073 | test loss: 4.1251 | test accuracy: 15.230 | epoch runtime:   4.00 sec | best accuracy: 15.230 @ epoch: 019\u001b[0m\n",
      "\u001b[32m2024-10-06 15:52:48,627 - INFO - Evaluate Summary Time 1.85s\tLoss 4.0561\t Acc@1 15.6100\t Acc@5 33.2600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:52:48,627 - INFO - Head 36.222\tMid 7.343\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:52:48,628 - INFO - epoch:  20 | train loss: 4.7080 | train accuracy: 31.087 | test loss: 4.0561 | test accuracy: 15.610 | epoch runtime:   4.00 sec | best accuracy: 15.610 @ epoch: 020\u001b[0m\n",
      "\u001b[32m2024-10-06 15:52:52,492 - INFO - Evaluate Summary Time 1.75s\tLoss 4.1079\t Acc@1 15.1400\t Acc@5 33.0800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:52:52,492 - INFO - Head 35.611\tMid 6.629\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:52:52,492 - INFO - epoch:  21 | train loss: 4.7117 | train accuracy: 31.778 | test loss: 4.1079 | test accuracy: 15.140 | epoch runtime:   3.86 sec | best accuracy: 15.610 @ epoch: 020\u001b[0m\n",
      "\u001b[32m2024-10-06 15:52:56,395 - INFO - Evaluate Summary Time 1.77s\tLoss 4.1272\t Acc@1 15.5900\t Acc@5 33.6700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:52:56,395 - INFO - Head 35.861\tMid 7.629\tTail 0.034\u001b[0m\n",
      "\u001b[32m2024-10-06 15:52:56,396 - INFO - epoch:  22 | train loss: 4.6853 | train accuracy: 32.719 | test loss: 4.1272 | test accuracy: 15.590 | epoch runtime:   3.90 sec | best accuracy: 15.610 @ epoch: 020\u001b[0m\n",
      "\u001b[32m2024-10-06 15:53:00,191 - INFO - Evaluate Summary Time 1.68s\tLoss 4.1146\t Acc@1 15.3400\t Acc@5 33.9800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:53:00,192 - INFO - Head 35.556\tMid 7.200\tTail 0.069\u001b[0m\n",
      "\u001b[32m2024-10-06 15:53:00,192 - INFO - epoch:  23 | train loss: 4.6662 | train accuracy: 34.470 | test loss: 4.1146 | test accuracy: 15.340 | epoch runtime:   3.80 sec | best accuracy: 15.610 @ epoch: 020\u001b[0m\n",
      "\u001b[32m2024-10-06 15:53:03,954 - INFO - Evaluate Summary Time 1.69s\tLoss 4.0620\t Acc@1 16.2300\t Acc@5 34.6700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:53:03,954 - INFO - Head 36.889\tMid 8.371\tTail 0.069\u001b[0m\n",
      "\u001b[32m2024-10-06 15:53:03,955 - INFO - epoch:  24 | train loss: 4.6680 | train accuracy: 35.060 | test loss: 4.0620 | test accuracy: 16.230 | epoch runtime:   3.76 sec | best accuracy: 16.230 @ epoch: 024\u001b[0m\n",
      "\u001b[32m2024-10-06 15:53:07,770 - INFO - Evaluate Summary Time 1.70s\tLoss 4.0165\t Acc@1 16.7000\t Acc@5 35.1200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:53:07,770 - INFO - Head 37.278\tMid 9.343\tTail 0.034\u001b[0m\n",
      "\u001b[32m2024-10-06 15:53:07,771 - INFO - epoch:  25 | train loss: 4.6605 | train accuracy: 35.992 | test loss: 4.0165 | test accuracy: 16.700 | epoch runtime:   3.82 sec | best accuracy: 16.700 @ epoch: 025\u001b[0m\n",
      "\u001b[32m2024-10-06 15:53:11,625 - INFO - Evaluate Summary Time 1.69s\tLoss 4.0933\t Acc@1 16.0300\t Acc@5 34.6500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:53:11,625 - INFO - Head 36.500\tMid 8.229\tTail 0.034\u001b[0m\n",
      "\u001b[32m2024-10-06 15:53:11,625 - INFO - epoch:  26 | train loss: 4.6470 | train accuracy: 37.365 | test loss: 4.0933 | test accuracy: 16.030 | epoch runtime:   3.85 sec | best accuracy: 16.700 @ epoch: 025\u001b[0m\n",
      "\u001b[32m2024-10-06 15:53:15,526 - INFO - Evaluate Summary Time 1.75s\tLoss 4.0577\t Acc@1 16.5200\t Acc@5 35.6700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:53:15,526 - INFO - Head 37.167\tMid 8.914\tTail 0.069\u001b[0m\n",
      "\u001b[32m2024-10-06 15:53:15,527 - INFO - epoch:  27 | train loss: 4.6315 | train accuracy: 38.822 | test loss: 4.0577 | test accuracy: 16.520 | epoch runtime:   3.90 sec | best accuracy: 16.700 @ epoch: 025\u001b[0m\n",
      "\u001b[32m2024-10-06 15:53:19,504 - INFO - Evaluate Summary Time 1.80s\tLoss 4.0198\t Acc@1 16.6800\t Acc@5 35.4800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:53:19,504 - INFO - Head 38.250\tMid 8.257\tTail 0.069\u001b[0m\n",
      "\u001b[32m2024-10-06 15:53:19,505 - INFO - epoch:  28 | train loss: 4.6225 | train accuracy: 39.771 | test loss: 4.0198 | test accuracy: 16.680 | epoch runtime:   3.98 sec | best accuracy: 16.700 @ epoch: 025\u001b[0m\n",
      "\u001b[32m2024-10-06 15:53:23,350 - INFO - Evaluate Summary Time 1.67s\tLoss 4.0589\t Acc@1 16.8900\t Acc@5 35.9100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:53:23,350 - INFO - Head 38.167\tMid 8.971\tTail 0.034\u001b[0m\n",
      "\u001b[32m2024-10-06 15:53:23,350 - INFO - epoch:  29 | train loss: 4.6165 | train accuracy: 40.398 | test loss: 4.0589 | test accuracy: 16.890 | epoch runtime:   3.85 sec | best accuracy: 16.890 @ epoch: 029\u001b[0m\n",
      "\u001b[32m2024-10-06 15:53:27,169 - INFO - Evaluate Summary Time 1.64s\tLoss 4.0596\t Acc@1 16.7000\t Acc@5 35.6200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:53:27,169 - INFO - Head 37.639\tMid 8.943\tTail 0.069\u001b[0m\n",
      "\u001b[32m2024-10-06 15:53:27,169 - INFO - epoch:  30 | train loss: 4.6033 | train accuracy: 42.353 | test loss: 4.0596 | test accuracy: 16.700 | epoch runtime:   3.82 sec | best accuracy: 16.890 @ epoch: 029\u001b[0m\n",
      "\u001b[32m2024-10-06 15:53:31,002 - INFO - Evaluate Summary Time 1.67s\tLoss 4.0818\t Acc@1 16.3200\t Acc@5 35.1500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:53:31,003 - INFO - Head 37.278\tMid 8.229\tTail 0.069\u001b[0m\n",
      "\u001b[32m2024-10-06 15:53:31,003 - INFO - epoch:  31 | train loss: 4.5948 | train accuracy: 43.477 | test loss: 4.0818 | test accuracy: 16.320 | epoch runtime:   3.83 sec | best accuracy: 16.890 @ epoch: 029\u001b[0m\n",
      "\u001b[32m2024-10-06 15:53:34,829 - INFO - Evaluate Summary Time 1.71s\tLoss 4.0335\t Acc@1 16.6100\t Acc@5 35.7100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:53:34,829 - INFO - Head 38.111\tMid 8.229\tTail 0.034\u001b[0m\n",
      "\u001b[32m2024-10-06 15:53:34,829 - INFO - epoch:  32 | train loss: 4.5922 | train accuracy: 44.538 | test loss: 4.0335 | test accuracy: 16.610 | epoch runtime:   3.83 sec | best accuracy: 16.890 @ epoch: 029\u001b[0m\n",
      "\u001b[32m2024-10-06 15:53:38,889 - INFO - Evaluate Summary Time 1.77s\tLoss 4.0389\t Acc@1 17.0000\t Acc@5 36.0100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:53:38,889 - INFO - Head 39.111\tMid 8.257\tTail 0.103\u001b[0m\n",
      "\u001b[32m2024-10-06 15:53:38,889 - INFO - epoch:  33 | train loss: 4.5780 | train accuracy: 45.967 | test loss: 4.0389 | test accuracy: 17.000 | epoch runtime:   4.06 sec | best accuracy: 17.000 @ epoch: 033\u001b[0m\n",
      "\u001b[32m2024-10-06 15:53:42,910 - INFO - Evaluate Summary Time 1.80s\tLoss 4.0652\t Acc@1 16.5200\t Acc@5 35.4100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:53:42,911 - INFO - Head 37.861\tMid 8.200\tTail 0.069\u001b[0m\n",
      "\u001b[32m2024-10-06 15:53:42,911 - INFO - epoch:  34 | train loss: 4.5667 | train accuracy: 47.626 | test loss: 4.0652 | test accuracy: 16.520 | epoch runtime:   4.02 sec | best accuracy: 17.000 @ epoch: 033\u001b[0m\n",
      "\u001b[32m2024-10-06 15:53:46,855 - INFO - Evaluate Summary Time 1.75s\tLoss 4.0408\t Acc@1 16.6000\t Acc@5 34.8300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:53:46,855 - INFO - Head 38.472\tMid 7.829\tTail 0.034\u001b[0m\n",
      "\u001b[32m2024-10-06 15:53:46,856 - INFO - epoch:  35 | train loss: 4.5748 | train accuracy: 48.742 | test loss: 4.0408 | test accuracy: 16.600 | epoch runtime:   3.94 sec | best accuracy: 17.000 @ epoch: 033\u001b[0m\n",
      "\u001b[32m2024-10-06 15:53:50,746 - INFO - Evaluate Summary Time 1.74s\tLoss 4.0345\t Acc@1 16.9900\t Acc@5 35.4900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:53:50,747 - INFO - Head 38.972\tMid 8.429\tTail 0.034\u001b[0m\n",
      "\u001b[32m2024-10-06 15:53:50,747 - INFO - epoch:  36 | train loss: 4.5583 | train accuracy: 49.562 | test loss: 4.0345 | test accuracy: 16.990 | epoch runtime:   3.89 sec | best accuracy: 17.000 @ epoch: 033\u001b[0m\n",
      "\u001b[32m2024-10-06 15:53:54,698 - INFO - Evaluate Summary Time 1.79s\tLoss 4.0271\t Acc@1 16.7100\t Acc@5 35.5400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:53:54,698 - INFO - Head 38.417\tMid 8.171\tTail 0.069\u001b[0m\n",
      "\u001b[32m2024-10-06 15:53:54,698 - INFO - epoch:  37 | train loss: 4.5402 | train accuracy: 51.636 | test loss: 4.0271 | test accuracy: 16.710 | epoch runtime:   3.95 sec | best accuracy: 17.000 @ epoch: 033\u001b[0m\n",
      "\u001b[32m2024-10-06 15:53:58,549 - INFO - Evaluate Summary Time 1.68s\tLoss 4.0167\t Acc@1 17.1000\t Acc@5 35.9000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:53:58,549 - INFO - Head 39.139\tMid 8.571\tTail 0.034\u001b[0m\n",
      "\u001b[32m2024-10-06 15:53:58,550 - INFO - epoch:  38 | train loss: 4.5493 | train accuracy: 53.158 | test loss: 4.0167 | test accuracy: 17.100 | epoch runtime:   3.85 sec | best accuracy: 17.100 @ epoch: 038\u001b[0m\n",
      "\u001b[32m2024-10-06 15:54:02,466 - INFO - Evaluate Summary Time 1.80s\tLoss 4.0194\t Acc@1 16.9000\t Acc@5 34.9000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:54:02,466 - INFO - Head 38.694\tMid 8.457\tTail 0.034\u001b[0m\n",
      "\u001b[32m2024-10-06 15:54:02,466 - INFO - epoch:  39 | train loss: 4.5207 | train accuracy: 55.066 | test loss: 4.0194 | test accuracy: 16.900 | epoch runtime:   3.92 sec | best accuracy: 17.100 @ epoch: 038\u001b[0m\n",
      "\u001b[32m2024-10-06 15:54:06,304 - INFO - Evaluate Summary Time 1.70s\tLoss 4.0264\t Acc@1 16.9300\t Acc@5 35.0800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:54:06,305 - INFO - Head 38.833\tMid 8.429\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:54:06,305 - INFO - epoch:  40 | train loss: 4.5110 | train accuracy: 56.956 | test loss: 4.0264 | test accuracy: 16.930 | epoch runtime:   3.84 sec | best accuracy: 17.100 @ epoch: 038\u001b[0m\n",
      "\u001b[32m2024-10-06 15:54:10,202 - INFO - Evaluate Summary Time 1.72s\tLoss 4.0051\t Acc@1 17.1200\t Acc@5 35.5900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:54:10,203 - INFO - Head 39.056\tMid 8.743\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:54:10,203 - INFO - epoch:  41 | train loss: 4.5041 | train accuracy: 58.329 | test loss: 4.0051 | test accuracy: 17.120 | epoch runtime:   3.90 sec | best accuracy: 17.120 @ epoch: 041\u001b[0m\n",
      "\u001b[32m2024-10-06 15:54:14,067 - INFO - Evaluate Summary Time 1.70s\tLoss 3.9768\t Acc@1 17.2700\t Acc@5 35.9600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:54:14,067 - INFO - Head 38.750\tMid 9.457\tTail 0.034\u001b[0m\n",
      "\u001b[32m2024-10-06 15:54:14,067 - INFO - epoch:  42 | train loss: 4.5065 | train accuracy: 59.703 | test loss: 3.9768 | test accuracy: 17.270 | epoch runtime:   3.86 sec | best accuracy: 17.270 @ epoch: 042\u001b[0m\n",
      "\u001b[32m2024-10-06 15:54:17,885 - INFO - Evaluate Summary Time 1.68s\tLoss 3.9913\t Acc@1 17.2600\t Acc@5 35.5800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:54:17,885 - INFO - Head 39.417\tMid 8.714\tTail 0.069\u001b[0m\n",
      "\u001b[32m2024-10-06 15:54:17,886 - INFO - epoch:  43 | train loss: 4.4832 | train accuracy: 61.694 | test loss: 3.9913 | test accuracy: 17.260 | epoch runtime:   3.82 sec | best accuracy: 17.270 @ epoch: 042\u001b[0m\n",
      "\u001b[32m2024-10-06 15:54:21,776 - INFO - Evaluate Summary Time 1.71s\tLoss 3.9816\t Acc@1 17.6900\t Acc@5 36.1300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:54:21,776 - INFO - Head 39.556\tMid 9.857\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:54:21,777 - INFO - epoch:  44 | train loss: 4.4715 | train accuracy: 64.469 | test loss: 3.9816 | test accuracy: 17.690 | epoch runtime:   3.89 sec | best accuracy: 17.690 @ epoch: 044\u001b[0m\n",
      "\u001b[32m2024-10-06 15:54:25,653 - INFO - Evaluate Summary Time 1.71s\tLoss 3.9738\t Acc@1 17.2800\t Acc@5 35.4600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:54:25,653 - INFO - Head 39.361\tMid 8.829\tTail 0.069\u001b[0m\n",
      "\u001b[32m2024-10-06 15:54:25,654 - INFO - epoch:  45 | train loss: 4.4616 | train accuracy: 66.405 | test loss: 3.9738 | test accuracy: 17.280 | epoch runtime:   3.88 sec | best accuracy: 17.690 @ epoch: 044\u001b[0m\n",
      "\u001b[32m2024-10-06 15:54:29,626 - INFO - Evaluate Summary Time 1.82s\tLoss 3.9597\t Acc@1 17.4600\t Acc@5 35.6300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:54:29,626 - INFO - Head 40.111\tMid 8.514\tTail 0.138\u001b[0m\n",
      "\u001b[32m2024-10-06 15:54:29,627 - INFO - epoch:  46 | train loss: 4.4536 | train accuracy: 68.083 | test loss: 3.9597 | test accuracy: 17.460 | epoch runtime:   3.97 sec | best accuracy: 17.690 @ epoch: 044\u001b[0m\n",
      "\u001b[32m2024-10-06 15:54:33,563 - INFO - Evaluate Summary Time 1.78s\tLoss 4.0010\t Acc@1 17.2200\t Acc@5 35.2500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:54:33,563 - INFO - Head 39.556\tMid 8.429\tTail 0.103\u001b[0m\n",
      "\u001b[32m2024-10-06 15:54:33,563 - INFO - epoch:  47 | train loss: 4.4395 | train accuracy: 70.573 | test loss: 4.0010 | test accuracy: 17.220 | epoch runtime:   3.94 sec | best accuracy: 17.690 @ epoch: 044\u001b[0m\n",
      "\u001b[32m2024-10-06 15:54:37,559 - INFO - Evaluate Summary Time 1.83s\tLoss 3.9746\t Acc@1 17.5200\t Acc@5 35.5400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:54:37,560 - INFO - Head 39.417\tMid 9.514\tTail 0.000\u001b[0m\n",
      "\u001b[32m2024-10-06 15:54:37,560 - INFO - epoch:  48 | train loss: 4.4282 | train accuracy: 72.324 | test loss: 3.9746 | test accuracy: 17.520 | epoch runtime:   4.00 sec | best accuracy: 17.690 @ epoch: 044\u001b[0m\n",
      "\u001b[32m2024-10-06 15:54:41,544 - INFO - Evaluate Summary Time 1.75s\tLoss 3.9647\t Acc@1 17.0600\t Acc@5 35.4000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:54:41,545 - INFO - Head 39.111\tMid 8.457\tTail 0.069\u001b[0m\n",
      "\u001b[32m2024-10-06 15:54:41,545 - INFO - epoch:  49 | train loss: 4.4244 | train accuracy: 73.845 | test loss: 3.9647 | test accuracy: 17.060 | epoch runtime:   3.98 sec | best accuracy: 17.690 @ epoch: 044\u001b[0m\n",
      "\u001b[32m2024-10-06 15:54:45,627 - INFO - Evaluate Summary Time 1.85s\tLoss 3.9519\t Acc@1 17.6800\t Acc@5 36.1100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:54:45,627 - INFO - Head 39.944\tMid 9.343\tTail 0.103\u001b[0m\n",
      "\u001b[32m2024-10-06 15:54:45,628 - INFO - epoch:  50 | train loss: 4.4290 | train accuracy: 74.970 | test loss: 3.9519 | test accuracy: 17.680 | epoch runtime:   4.08 sec | best accuracy: 17.690 @ epoch: 044\u001b[0m\n",
      "\u001b[32m2024-10-06 15:54:49,456 - INFO - Evaluate Summary Time 1.70s\tLoss 3.9653\t Acc@1 17.4500\t Acc@5 35.5800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:54:49,456 - INFO - Head 39.444\tMid 9.200\tTail 0.103\u001b[0m\n",
      "\u001b[32m2024-10-06 15:54:49,457 - INFO - epoch:  51 | train loss: 4.4040 | train accuracy: 77.367 | test loss: 3.9653 | test accuracy: 17.450 | epoch runtime:   3.83 sec | best accuracy: 17.690 @ epoch: 044\u001b[0m\n",
      "\u001b[32m2024-10-06 15:54:53,372 - INFO - Evaluate Summary Time 1.76s\tLoss 3.9364\t Acc@1 17.2000\t Acc@5 36.0800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:54:53,372 - INFO - Head 38.889\tMid 9.057\tTail 0.103\u001b[0m\n",
      "\u001b[32m2024-10-06 15:54:53,372 - INFO - epoch:  52 | train loss: 4.3958 | train accuracy: 78.999 | test loss: 3.9364 | test accuracy: 17.200 | epoch runtime:   3.92 sec | best accuracy: 17.690 @ epoch: 044\u001b[0m\n",
      "\u001b[32m2024-10-06 15:54:57,298 - INFO - Evaluate Summary Time 1.75s\tLoss 3.9518\t Acc@1 17.5100\t Acc@5 35.6100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:54:57,298 - INFO - Head 39.528\tMid 9.343\tTail 0.034\u001b[0m\n",
      "\u001b[32m2024-10-06 15:54:57,299 - INFO - epoch:  53 | train loss: 4.3828 | train accuracy: 80.898 | test loss: 3.9518 | test accuracy: 17.510 | epoch runtime:   3.93 sec | best accuracy: 17.690 @ epoch: 044\u001b[0m\n",
      "\u001b[32m2024-10-06 15:55:01,125 - INFO - Evaluate Summary Time 1.71s\tLoss 3.9443\t Acc@1 17.6400\t Acc@5 35.6600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:55:01,125 - INFO - Head 39.667\tMid 9.486\tTail 0.138\u001b[0m\n",
      "\u001b[32m2024-10-06 15:55:01,126 - INFO - epoch:  54 | train loss: 4.3741 | train accuracy: 82.742 | test loss: 3.9443 | test accuracy: 17.640 | epoch runtime:   3.83 sec | best accuracy: 17.690 @ epoch: 044\u001b[0m\n",
      "\u001b[32m2024-10-06 15:55:05,088 - INFO - Evaluate Summary Time 1.79s\tLoss 3.9549\t Acc@1 17.2800\t Acc@5 35.3800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:55:05,088 - INFO - Head 39.083\tMid 9.086\tTail 0.103\u001b[0m\n",
      "\u001b[32m2024-10-06 15:55:05,088 - INFO - epoch:  55 | train loss: 4.3654 | train accuracy: 84.235 | test loss: 3.9549 | test accuracy: 17.280 | epoch runtime:   3.96 sec | best accuracy: 17.690 @ epoch: 044\u001b[0m\n",
      "\u001b[32m2024-10-06 15:55:08,965 - INFO - Evaluate Summary Time 1.68s\tLoss 3.9361\t Acc@1 17.4900\t Acc@5 35.9600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:55:08,965 - INFO - Head 39.222\tMid 9.457\tTail 0.207\u001b[0m\n",
      "\u001b[32m2024-10-06 15:55:08,965 - INFO - epoch:  56 | train loss: 4.3604 | train accuracy: 85.148 | test loss: 3.9361 | test accuracy: 17.490 | epoch runtime:   3.88 sec | best accuracy: 17.690 @ epoch: 044\u001b[0m\n",
      "\u001b[32m2024-10-06 15:55:12,836 - INFO - Evaluate Summary Time 1.69s\tLoss 3.9516\t Acc@1 17.5900\t Acc@5 35.3700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:55:12,837 - INFO - Head 39.194\tMid 9.914\tTail 0.034\u001b[0m\n",
      "\u001b[32m2024-10-06 15:55:12,837 - INFO - epoch:  57 | train loss: 4.3492 | train accuracy: 87.075 | test loss: 3.9516 | test accuracy: 17.590 | epoch runtime:   3.87 sec | best accuracy: 17.690 @ epoch: 044\u001b[0m\n",
      "\u001b[32m2024-10-06 15:55:16,854 - INFO - Evaluate Summary Time 1.81s\tLoss 3.9351\t Acc@1 17.5700\t Acc@5 35.9100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:55:16,855 - INFO - Head 39.000\tMid 9.857\tTail 0.276\u001b[0m\n",
      "\u001b[32m2024-10-06 15:55:16,855 - INFO - epoch:  58 | train loss: 4.3432 | train accuracy: 87.987 | test loss: 3.9351 | test accuracy: 17.570 | epoch runtime:   4.02 sec | best accuracy: 17.690 @ epoch: 044\u001b[0m\n",
      "\u001b[32m2024-10-06 15:55:20,649 - INFO - Evaluate Summary Time 1.69s\tLoss 3.9537\t Acc@1 17.0900\t Acc@5 35.3200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:55:20,649 - INFO - Head 38.833\tMid 8.771\tTail 0.138\u001b[0m\n",
      "\u001b[32m2024-10-06 15:55:20,650 - INFO - epoch:  59 | train loss: 4.3400 | train accuracy: 89.241 | test loss: 3.9537 | test accuracy: 17.090 | epoch runtime:   3.79 sec | best accuracy: 17.690 @ epoch: 044\u001b[0m\n",
      "\u001b[32m2024-10-06 15:55:24,553 - INFO - Evaluate Summary Time 1.79s\tLoss 3.9358\t Acc@1 17.6200\t Acc@5 35.8100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:55:24,554 - INFO - Head 39.278\tMid 9.829\tTail 0.138\u001b[0m\n",
      "\u001b[32m2024-10-06 15:55:24,554 - INFO - epoch:  60 | train loss: 4.3307 | train accuracy: 90.218 | test loss: 3.9358 | test accuracy: 17.620 | epoch runtime:   3.90 sec | best accuracy: 17.690 @ epoch: 044\u001b[0m\n",
      "\u001b[32m2024-10-06 15:55:28,411 - INFO - Evaluate Summary Time 1.71s\tLoss 3.9318\t Acc@1 17.6800\t Acc@5 35.4800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:55:28,412 - INFO - Head 39.333\tMid 9.800\tTail 0.310\u001b[0m\n",
      "\u001b[32m2024-10-06 15:55:28,412 - INFO - epoch:  61 | train loss: 4.3205 | train accuracy: 91.187 | test loss: 3.9318 | test accuracy: 17.680 | epoch runtime:   3.86 sec | best accuracy: 17.690 @ epoch: 044\u001b[0m\n",
      "\u001b[32m2024-10-06 15:55:32,295 - INFO - Evaluate Summary Time 1.72s\tLoss 3.9529\t Acc@1 17.6600\t Acc@5 35.7200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:55:32,296 - INFO - Head 39.694\tMid 9.486\tTail 0.172\u001b[0m\n",
      "\u001b[32m2024-10-06 15:55:32,296 - INFO - epoch:  62 | train loss: 4.3159 | train accuracy: 91.767 | test loss: 3.9529 | test accuracy: 17.660 | epoch runtime:   3.88 sec | best accuracy: 17.690 @ epoch: 044\u001b[0m\n",
      "\u001b[32m2024-10-06 15:55:36,208 - INFO - Evaluate Summary Time 1.79s\tLoss 3.9397\t Acc@1 17.6100\t Acc@5 34.9700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:55:36,208 - INFO - Head 39.167\tMid 9.800\tTail 0.276\u001b[0m\n",
      "\u001b[32m2024-10-06 15:55:36,208 - INFO - epoch:  63 | train loss: 4.3091 | train accuracy: 92.754 | test loss: 3.9397 | test accuracy: 17.610 | epoch runtime:   3.91 sec | best accuracy: 17.690 @ epoch: 044\u001b[0m\n",
      "\u001b[32m2024-10-06 15:55:40,234 - INFO - Evaluate Summary Time 1.78s\tLoss 3.9454\t Acc@1 17.6900\t Acc@5 35.5500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:55:40,235 - INFO - Head 39.500\tMid 9.629\tTail 0.345\u001b[0m\n",
      "\u001b[32m2024-10-06 15:55:40,235 - INFO - epoch:  64 | train loss: 4.3015 | train accuracy: 93.685 | test loss: 3.9454 | test accuracy: 17.690 | epoch runtime:   4.03 sec | best accuracy: 17.690 @ epoch: 044\u001b[0m\n",
      "\u001b[32m2024-10-06 15:55:44,193 - INFO - Evaluate Summary Time 1.73s\tLoss 3.9454\t Acc@1 17.6300\t Acc@5 35.3700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:55:44,194 - INFO - Head 39.639\tMid 9.371\tTail 0.276\u001b[0m\n",
      "\u001b[32m2024-10-06 15:55:44,194 - INFO - epoch:  65 | train loss: 4.2979 | train accuracy: 94.109 | test loss: 3.9454 | test accuracy: 17.630 | epoch runtime:   3.96 sec | best accuracy: 17.690 @ epoch: 044\u001b[0m\n",
      "\u001b[32m2024-10-06 15:55:48,120 - INFO - Evaluate Summary Time 1.77s\tLoss 3.9238\t Acc@1 17.9800\t Acc@5 35.8200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:55:48,120 - INFO - Head 39.333\tMid 10.686\tTail 0.276\u001b[0m\n",
      "\u001b[32m2024-10-06 15:55:48,121 - INFO - epoch:  66 | train loss: 4.2902 | train accuracy: 94.773 | test loss: 3.9238 | test accuracy: 17.980 | epoch runtime:   3.93 sec | best accuracy: 17.980 @ epoch: 066\u001b[0m\n",
      "\u001b[32m2024-10-06 15:55:52,099 - INFO - Evaluate Summary Time 1.77s\tLoss 3.9379\t Acc@1 17.5700\t Acc@5 35.3000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:55:52,099 - INFO - Head 39.417\tMid 9.486\tTail 0.207\u001b[0m\n",
      "\u001b[32m2024-10-06 15:55:52,100 - INFO - epoch:  67 | train loss: 4.2877 | train accuracy: 95.307 | test loss: 3.9379 | test accuracy: 17.570 | epoch runtime:   3.98 sec | best accuracy: 17.980 @ epoch: 066\u001b[0m\n",
      "\u001b[32m2024-10-06 15:55:56,096 - INFO - Evaluate Summary Time 1.79s\tLoss 3.9558\t Acc@1 17.6600\t Acc@5 35.1100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:55:56,096 - INFO - Head 38.750\tMid 10.314\tTail 0.345\u001b[0m\n",
      "\u001b[32m2024-10-06 15:55:56,096 - INFO - epoch:  68 | train loss: 4.2819 | train accuracy: 95.704 | test loss: 3.9558 | test accuracy: 17.660 | epoch runtime:   4.00 sec | best accuracy: 17.980 @ epoch: 066\u001b[0m\n",
      "\u001b[32m2024-10-06 15:56:00,051 - INFO - Evaluate Summary Time 1.78s\tLoss 3.9393\t Acc@1 17.4100\t Acc@5 35.0800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:56:00,052 - INFO - Head 38.111\tMid 10.229\tTail 0.379\u001b[0m\n",
      "\u001b[32m2024-10-06 15:56:00,052 - INFO - epoch:  69 | train loss: 4.2750 | train accuracy: 96.054 | test loss: 3.9393 | test accuracy: 17.410 | epoch runtime:   3.96 sec | best accuracy: 17.980 @ epoch: 066\u001b[0m\n",
      "\u001b[32m2024-10-06 15:56:03,829 - INFO - Evaluate Summary Time 1.69s\tLoss 3.9151\t Acc@1 17.8200\t Acc@5 35.4300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:56:03,829 - INFO - Head 37.778\tMid 11.657\tTail 0.483\u001b[0m\n",
      "\u001b[32m2024-10-06 15:56:03,829 - INFO - epoch:  70 | train loss: 4.2785 | train accuracy: 96.386 | test loss: 3.9151 | test accuracy: 17.820 | epoch runtime:   3.78 sec | best accuracy: 17.980 @ epoch: 066\u001b[0m\n",
      "\u001b[32m2024-10-06 15:56:07,729 - INFO - Evaluate Summary Time 1.71s\tLoss 3.9389\t Acc@1 16.9900\t Acc@5 34.1500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:56:07,730 - INFO - Head 35.667\tMid 11.343\tTail 0.621\u001b[0m\n",
      "\u001b[32m2024-10-06 15:56:07,730 - INFO - epoch:  71 | train loss: 4.3594 | train accuracy: 94.616 | test loss: 3.9389 | test accuracy: 16.990 | epoch runtime:   3.90 sec | best accuracy: 17.980 @ epoch: 066\u001b[0m\n",
      "\u001b[32m2024-10-06 15:56:11,485 - INFO - Evaluate Summary Time 1.69s\tLoss 3.9311\t Acc@1 17.9700\t Acc@5 35.4100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:56:11,485 - INFO - Head 39.472\tMid 10.543\tTail 0.241\u001b[0m\n",
      "\u001b[32m2024-10-06 15:56:11,486 - INFO - epoch:  72 | train loss: 4.3017 | train accuracy: 95.575 | test loss: 3.9311 | test accuracy: 17.970 | epoch runtime:   3.76 sec | best accuracy: 17.980 @ epoch: 066\u001b[0m\n",
      "\u001b[32m2024-10-06 15:56:15,509 - INFO - Evaluate Summary Time 1.79s\tLoss 3.9304\t Acc@1 17.9900\t Acc@5 35.3000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:56:15,509 - INFO - Head 39.139\tMid 10.971\tTail 0.207\u001b[0m\n",
      "\u001b[32m2024-10-06 15:56:15,510 - INFO - epoch:  73 | train loss: 4.2627 | train accuracy: 97.114 | test loss: 3.9304 | test accuracy: 17.990 | epoch runtime:   4.02 sec | best accuracy: 17.990 @ epoch: 073\u001b[0m\n",
      "\u001b[32m2024-10-06 15:56:19,442 - INFO - Evaluate Summary Time 1.73s\tLoss 3.9538\t Acc@1 17.4800\t Acc@5 34.7800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:56:19,443 - INFO - Head 39.056\tMid 9.571\tTail 0.241\u001b[0m\n",
      "\u001b[32m2024-10-06 15:56:19,443 - INFO - epoch:  74 | train loss: 4.2603 | train accuracy: 97.382 | test loss: 3.9538 | test accuracy: 17.480 | epoch runtime:   3.93 sec | best accuracy: 17.990 @ epoch: 073\u001b[0m\n",
      "\u001b[32m2024-10-06 15:56:23,481 - INFO - Evaluate Summary Time 1.80s\tLoss 3.9341\t Acc@1 18.0600\t Acc@5 35.9100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:56:23,482 - INFO - Head 39.167\tMid 10.943\tTail 0.448\u001b[0m\n",
      "\u001b[32m2024-10-06 15:56:23,482 - INFO - epoch:  75 | train loss: 4.2531 | train accuracy: 97.594 | test loss: 3.9341 | test accuracy: 18.060 | epoch runtime:   4.04 sec | best accuracy: 18.060 @ epoch: 075\u001b[0m\n",
      "\u001b[32m2024-10-06 15:56:27,372 - INFO - Evaluate Summary Time 1.75s\tLoss 3.9175\t Acc@1 17.9000\t Acc@5 35.5800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:56:27,372 - INFO - Head 39.361\tMid 10.400\tTail 0.310\u001b[0m\n",
      "\u001b[32m2024-10-06 15:56:27,372 - INFO - epoch:  76 | train loss: 4.2506 | train accuracy: 97.741 | test loss: 3.9175 | test accuracy: 17.900 | epoch runtime:   3.89 sec | best accuracy: 18.060 @ epoch: 075\u001b[0m\n",
      "\u001b[32m2024-10-06 15:56:31,316 - INFO - Evaluate Summary Time 1.74s\tLoss 3.9126\t Acc@1 18.0700\t Acc@5 35.9200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:56:31,316 - INFO - Head 38.833\tMid 11.400\tTail 0.345\u001b[0m\n",
      "\u001b[32m2024-10-06 15:56:31,316 - INFO - epoch:  77 | train loss: 4.2486 | train accuracy: 98.027 | test loss: 3.9126 | test accuracy: 18.070 | epoch runtime:   3.94 sec | best accuracy: 18.070 @ epoch: 077\u001b[0m\n",
      "\u001b[32m2024-10-06 15:56:35,157 - INFO - Evaluate Summary Time 1.69s\tLoss 3.9352\t Acc@1 17.6900\t Acc@5 35.0600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:56:35,158 - INFO - Head 38.139\tMid 10.914\tTail 0.483\u001b[0m\n",
      "\u001b[32m2024-10-06 15:56:35,158 - INFO - epoch:  78 | train loss: 4.2409 | train accuracy: 98.165 | test loss: 3.9352 | test accuracy: 17.690 | epoch runtime:   3.84 sec | best accuracy: 18.070 @ epoch: 077\u001b[0m\n",
      "\u001b[32m2024-10-06 15:56:39,103 - INFO - Evaluate Summary Time 1.73s\tLoss 3.9360\t Acc@1 17.8500\t Acc@5 35.2200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:56:39,104 - INFO - Head 38.750\tMid 10.743\tTail 0.483\u001b[0m\n",
      "\u001b[32m2024-10-06 15:56:39,104 - INFO - epoch:  79 | train loss: 4.2459 | train accuracy: 98.073 | test loss: 3.9360 | test accuracy: 17.850 | epoch runtime:   3.95 sec | best accuracy: 18.070 @ epoch: 077\u001b[0m\n",
      "\u001b[32m2024-10-06 15:56:42,984 - INFO - Evaluate Summary Time 1.76s\tLoss 3.9376\t Acc@1 17.9300\t Acc@5 35.3400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:56:42,985 - INFO - Head 39.250\tMid 10.514\tTail 0.414\u001b[0m\n",
      "\u001b[32m2024-10-06 15:56:42,985 - INFO - epoch:  80 | train loss: 4.2376 | train accuracy: 98.313 | test loss: 3.9376 | test accuracy: 17.930 | epoch runtime:   3.88 sec | best accuracy: 18.070 @ epoch: 077\u001b[0m\n",
      "\u001b[32m2024-10-06 15:56:46,879 - INFO - Evaluate Summary Time 1.68s\tLoss 3.9084\t Acc@1 18.1800\t Acc@5 35.8800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:56:46,879 - INFO - Head 38.861\tMid 11.571\tTail 0.483\u001b[0m\n",
      "\u001b[32m2024-10-06 15:56:46,880 - INFO - epoch:  81 | train loss: 4.2134 | train accuracy: 98.995 | test loss: 3.9084 | test accuracy: 18.180 | epoch runtime:   3.89 sec | best accuracy: 18.180 @ epoch: 081\u001b[0m\n",
      "\u001b[32m2024-10-06 15:56:50,723 - INFO - Evaluate Summary Time 1.65s\tLoss 3.9027\t Acc@1 18.3200\t Acc@5 35.8300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:56:50,723 - INFO - Head 38.833\tMid 12.057\tTail 0.414\u001b[0m\n",
      "\u001b[32m2024-10-06 15:56:50,723 - INFO - epoch:  82 | train loss: 4.2054 | train accuracy: 99.078 | test loss: 3.9027 | test accuracy: 18.320 | epoch runtime:   3.84 sec | best accuracy: 18.320 @ epoch: 082\u001b[0m\n",
      "\u001b[32m2024-10-06 15:56:54,570 - INFO - Evaluate Summary Time 1.79s\tLoss 3.9117\t Acc@1 18.2800\t Acc@5 35.5700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:56:54,570 - INFO - Head 39.167\tMid 11.571\tTail 0.448\u001b[0m\n",
      "\u001b[32m2024-10-06 15:56:54,570 - INFO - epoch:  83 | train loss: 4.2041 | train accuracy: 99.189 | test loss: 3.9117 | test accuracy: 18.280 | epoch runtime:   3.85 sec | best accuracy: 18.320 @ epoch: 082\u001b[0m\n",
      "\u001b[32m2024-10-06 15:56:58,500 - INFO - Evaluate Summary Time 1.76s\tLoss 3.9228\t Acc@1 17.9500\t Acc@5 35.4800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:56:58,500 - INFO - Head 38.611\tMid 11.229\tTail 0.414\u001b[0m\n",
      "\u001b[32m2024-10-06 15:56:58,500 - INFO - epoch:  84 | train loss: 4.2037 | train accuracy: 99.170 | test loss: 3.9228 | test accuracy: 17.950 | epoch runtime:   3.93 sec | best accuracy: 18.320 @ epoch: 082\u001b[0m\n",
      "\u001b[32m2024-10-06 15:57:02,306 - INFO - Evaluate Summary Time 1.67s\tLoss 3.9243\t Acc@1 18.1900\t Acc@5 35.4400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:57:02,307 - INFO - Head 38.778\tMid 11.629\tTail 0.552\u001b[0m\n",
      "\u001b[32m2024-10-06 15:57:02,307 - INFO - epoch:  85 | train loss: 4.2043 | train accuracy: 99.262 | test loss: 3.9243 | test accuracy: 18.190 | epoch runtime:   3.81 sec | best accuracy: 18.320 @ epoch: 082\u001b[0m\n",
      "\u001b[32m2024-10-06 15:57:06,280 - INFO - Evaluate Summary Time 1.76s\tLoss 3.9123\t Acc@1 18.3400\t Acc@5 35.4500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:57:06,280 - INFO - Head 38.167\tMid 12.600\tTail 0.655\u001b[0m\n",
      "\u001b[32m2024-10-06 15:57:06,280 - INFO - epoch:  86 | train loss: 4.2005 | train accuracy: 99.373 | test loss: 3.9123 | test accuracy: 18.340 | epoch runtime:   3.97 sec | best accuracy: 18.340 @ epoch: 086\u001b[0m\n",
      "\u001b[32m2024-10-06 15:57:10,163 - INFO - Evaluate Summary Time 1.65s\tLoss 3.9098\t Acc@1 18.2800\t Acc@5 35.4800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:57:10,164 - INFO - Head 38.972\tMid 11.743\tTail 0.483\u001b[0m\n",
      "\u001b[32m2024-10-06 15:57:10,164 - INFO - epoch:  87 | train loss: 4.1999 | train accuracy: 99.484 | test loss: 3.9098 | test accuracy: 18.280 | epoch runtime:   3.88 sec | best accuracy: 18.340 @ epoch: 086\u001b[0m\n",
      "\u001b[32m2024-10-06 15:57:13,985 - INFO - Evaluate Summary Time 1.73s\tLoss 3.9159\t Acc@1 18.0500\t Acc@5 35.3200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:57:13,985 - INFO - Head 38.222\tMid 11.914\tTail 0.414\u001b[0m\n",
      "\u001b[32m2024-10-06 15:57:13,985 - INFO - epoch:  88 | train loss: 4.1953 | train accuracy: 99.475 | test loss: 3.9159 | test accuracy: 18.050 | epoch runtime:   3.82 sec | best accuracy: 18.340 @ epoch: 086\u001b[0m\n",
      "\u001b[32m2024-10-06 15:57:17,906 - INFO - Evaluate Summary Time 1.70s\tLoss 3.9311\t Acc@1 18.1300\t Acc@5 35.4700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:57:17,907 - INFO - Head 38.667\tMid 11.714\tTail 0.379\u001b[0m\n",
      "\u001b[32m2024-10-06 15:57:17,907 - INFO - epoch:  89 | train loss: 4.1960 | train accuracy: 99.484 | test loss: 3.9311 | test accuracy: 18.130 | epoch runtime:   3.92 sec | best accuracy: 18.340 @ epoch: 086\u001b[0m\n",
      "\u001b[32m2024-10-06 15:57:21,776 - INFO - Evaluate Summary Time 1.67s\tLoss 3.9232\t Acc@1 18.1500\t Acc@5 35.3500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:57:21,776 - INFO - Head 39.000\tMid 11.257\tTail 0.586\u001b[0m\n",
      "\u001b[32m2024-10-06 15:57:21,777 - INFO - epoch:  90 | train loss: 4.1917 | train accuracy: 99.539 | test loss: 3.9232 | test accuracy: 18.150 | epoch runtime:   3.87 sec | best accuracy: 18.340 @ epoch: 086\u001b[0m\n",
      "\u001b[32m2024-10-06 15:57:25,740 - INFO - Evaluate Summary Time 1.76s\tLoss 3.9225\t Acc@1 18.2500\t Acc@5 35.4200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:57:25,740 - INFO - Head 38.861\tMid 11.686\tTail 0.586\u001b[0m\n",
      "\u001b[32m2024-10-06 15:57:25,741 - INFO - epoch:  91 | train loss: 4.1943 | train accuracy: 99.438 | test loss: 3.9225 | test accuracy: 18.250 | epoch runtime:   3.96 sec | best accuracy: 18.340 @ epoch: 086\u001b[0m\n",
      "\u001b[32m2024-10-06 15:57:29,658 - INFO - Evaluate Summary Time 1.74s\tLoss 3.9179\t Acc@1 18.1700\t Acc@5 35.5600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:57:29,659 - INFO - Head 38.833\tMid 11.514\tTail 0.552\u001b[0m\n",
      "\u001b[32m2024-10-06 15:57:29,659 - INFO - epoch:  92 | train loss: 4.1927 | train accuracy: 99.502 | test loss: 3.9179 | test accuracy: 18.170 | epoch runtime:   3.92 sec | best accuracy: 18.340 @ epoch: 086\u001b[0m\n",
      "\u001b[32m2024-10-06 15:57:33,539 - INFO - Evaluate Summary Time 1.72s\tLoss 3.9186\t Acc@1 17.9800\t Acc@5 35.1700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:57:33,540 - INFO - Head 38.111\tMid 11.714\tTail 0.552\u001b[0m\n",
      "\u001b[32m2024-10-06 15:57:33,540 - INFO - epoch:  93 | train loss: 4.1900 | train accuracy: 99.604 | test loss: 3.9186 | test accuracy: 17.980 | epoch runtime:   3.88 sec | best accuracy: 18.340 @ epoch: 086\u001b[0m\n",
      "\u001b[32m2024-10-06 15:57:37,469 - INFO - Evaluate Summary Time 1.75s\tLoss 3.9261\t Acc@1 18.0500\t Acc@5 35.1300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:57:37,469 - INFO - Head 38.389\tMid 11.571\tTail 0.621\u001b[0m\n",
      "\u001b[32m2024-10-06 15:57:37,469 - INFO - epoch:  94 | train loss: 4.1882 | train accuracy: 99.548 | test loss: 3.9261 | test accuracy: 18.050 | epoch runtime:   3.93 sec | best accuracy: 18.340 @ epoch: 086\u001b[0m\n",
      "\u001b[32m2024-10-06 15:57:41,307 - INFO - Evaluate Summary Time 1.65s\tLoss 3.9271\t Acc@1 18.1100\t Acc@5 35.3800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:57:41,308 - INFO - Head 38.917\tMid 11.229\tTail 0.586\u001b[0m\n",
      "\u001b[32m2024-10-06 15:57:41,308 - INFO - epoch:  95 | train loss: 4.1863 | train accuracy: 99.567 | test loss: 3.9271 | test accuracy: 18.110 | epoch runtime:   3.84 sec | best accuracy: 18.340 @ epoch: 086\u001b[0m\n",
      "\u001b[32m2024-10-06 15:57:45,173 - INFO - Evaluate Summary Time 1.68s\tLoss 3.9177\t Acc@1 18.3200\t Acc@5 35.3700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:57:45,174 - INFO - Head 39.056\tMid 11.714\tTail 0.552\u001b[0m\n",
      "\u001b[32m2024-10-06 15:57:45,174 - INFO - epoch:  96 | train loss: 4.1856 | train accuracy: 99.714 | test loss: 3.9177 | test accuracy: 18.320 | epoch runtime:   3.87 sec | best accuracy: 18.340 @ epoch: 086\u001b[0m\n",
      "\u001b[32m2024-10-06 15:57:49,112 - INFO - Evaluate Summary Time 1.74s\tLoss 3.9227\t Acc@1 18.4300\t Acc@5 35.1800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:57:49,112 - INFO - Head 39.444\tMid 11.657\tTail 0.517\u001b[0m\n",
      "\u001b[32m2024-10-06 15:57:49,113 - INFO - epoch:  97 | train loss: 4.1854 | train accuracy: 99.604 | test loss: 3.9227 | test accuracy: 18.430 | epoch runtime:   3.94 sec | best accuracy: 18.430 @ epoch: 097\u001b[0m\n",
      "\u001b[32m2024-10-06 15:57:52,980 - INFO - Evaluate Summary Time 1.70s\tLoss 3.9293\t Acc@1 18.1800\t Acc@5 35.1500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:57:52,981 - INFO - Head 39.083\tMid 11.314\tTail 0.517\u001b[0m\n",
      "\u001b[32m2024-10-06 15:57:52,981 - INFO - epoch:  98 | train loss: 4.1815 | train accuracy: 99.585 | test loss: 3.9293 | test accuracy: 18.180 | epoch runtime:   3.87 sec | best accuracy: 18.430 @ epoch: 097\u001b[0m\n",
      "\u001b[32m2024-10-06 15:57:56,943 - INFO - Evaluate Summary Time 1.77s\tLoss 3.9306\t Acc@1 18.3700\t Acc@5 35.1200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:57:56,944 - INFO - Head 39.194\tMid 11.714\tTail 0.552\u001b[0m\n",
      "\u001b[32m2024-10-06 15:57:56,944 - INFO - epoch:  99 | train loss: 4.1837 | train accuracy: 99.659 | test loss: 3.9306 | test accuracy: 18.370 | epoch runtime:   3.96 sec | best accuracy: 18.430 @ epoch: 097\u001b[0m\n",
      "\u001b[32m2024-10-06 15:58:00,801 - INFO - Evaluate Summary Time 1.67s\tLoss 3.9223\t Acc@1 18.2800\t Acc@5 35.2300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:58:00,801 - INFO - Head 38.944\tMid 11.743\tTail 0.517\u001b[0m\n",
      "\u001b[32m2024-10-06 15:58:00,801 - INFO - epoch: 100 | train loss: 4.1817 | train accuracy: 99.677 | test loss: 3.9223 | test accuracy: 18.280 | epoch runtime:   3.86 sec | best accuracy: 18.430 @ epoch: 097\u001b[0m\n",
      "\u001b[32m2024-10-06 15:58:04,718 - INFO - Evaluate Summary Time 1.73s\tLoss 3.9203\t Acc@1 18.3600\t Acc@5 35.4200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:58:04,718 - INFO - Head 39.528\tMid 11.314\tTail 0.586\u001b[0m\n",
      "\u001b[32m2024-10-06 15:58:04,718 - INFO - epoch: 101 | train loss: 4.1800 | train accuracy: 99.723 | test loss: 3.9203 | test accuracy: 18.360 | epoch runtime:   3.92 sec | best accuracy: 18.430 @ epoch: 097\u001b[0m\n",
      "\u001b[32m2024-10-06 15:58:08,673 - INFO - Evaluate Summary Time 1.77s\tLoss 3.9209\t Acc@1 18.0900\t Acc@5 35.2100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:58:08,673 - INFO - Head 38.472\tMid 11.629\tTail 0.586\u001b[0m\n",
      "\u001b[32m2024-10-06 15:58:08,674 - INFO - epoch: 102 | train loss: 4.1795 | train accuracy: 99.714 | test loss: 3.9209 | test accuracy: 18.090 | epoch runtime:   3.96 sec | best accuracy: 18.430 @ epoch: 097\u001b[0m\n",
      "\u001b[32m2024-10-06 15:58:12,623 - INFO - Evaluate Summary Time 1.79s\tLoss 3.9259\t Acc@1 18.0800\t Acc@5 35.2300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:58:12,623 - INFO - Head 38.528\tMid 11.657\tTail 0.448\u001b[0m\n",
      "\u001b[32m2024-10-06 15:58:12,624 - INFO - epoch: 103 | train loss: 4.1744 | train accuracy: 99.677 | test loss: 3.9259 | test accuracy: 18.080 | epoch runtime:   3.95 sec | best accuracy: 18.430 @ epoch: 097\u001b[0m\n",
      "\u001b[32m2024-10-06 15:58:16,520 - INFO - Evaluate Summary Time 1.71s\tLoss 3.9196\t Acc@1 18.3200\t Acc@5 35.4600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:58:16,520 - INFO - Head 38.639\tMid 12.143\tTail 0.552\u001b[0m\n",
      "\u001b[32m2024-10-06 15:58:16,521 - INFO - epoch: 104 | train loss: 4.1816 | train accuracy: 99.751 | test loss: 3.9196 | test accuracy: 18.320 | epoch runtime:   3.90 sec | best accuracy: 18.430 @ epoch: 097\u001b[0m\n",
      "\u001b[32m2024-10-06 15:58:20,459 - INFO - Evaluate Summary Time 1.71s\tLoss 3.9248\t Acc@1 18.1100\t Acc@5 35.2800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:58:20,460 - INFO - Head 38.472\tMid 11.714\tTail 0.552\u001b[0m\n",
      "\u001b[32m2024-10-06 15:58:20,460 - INFO - epoch: 105 | train loss: 4.1770 | train accuracy: 99.797 | test loss: 3.9248 | test accuracy: 18.110 | epoch runtime:   3.94 sec | best accuracy: 18.430 @ epoch: 097\u001b[0m\n",
      "\u001b[32m2024-10-06 15:58:24,345 - INFO - Evaluate Summary Time 1.75s\tLoss 3.9220\t Acc@1 18.3900\t Acc@5 35.3600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:58:24,346 - INFO - Head 39.028\tMid 12.000\tTail 0.483\u001b[0m\n",
      "\u001b[32m2024-10-06 15:58:24,346 - INFO - epoch: 106 | train loss: 4.1737 | train accuracy: 99.788 | test loss: 3.9220 | test accuracy: 18.390 | epoch runtime:   3.89 sec | best accuracy: 18.430 @ epoch: 097\u001b[0m\n",
      "\u001b[32m2024-10-06 15:58:28,223 - INFO - Evaluate Summary Time 1.67s\tLoss 3.9277\t Acc@1 18.2200\t Acc@5 35.0900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:58:28,223 - INFO - Head 38.694\tMid 11.857\tTail 0.483\u001b[0m\n",
      "\u001b[32m2024-10-06 15:58:28,224 - INFO - epoch: 107 | train loss: 4.1733 | train accuracy: 99.751 | test loss: 3.9277 | test accuracy: 18.220 | epoch runtime:   3.88 sec | best accuracy: 18.430 @ epoch: 097\u001b[0m\n",
      "\u001b[32m2024-10-06 15:58:32,147 - INFO - Evaluate Summary Time 1.75s\tLoss 3.9241\t Acc@1 18.1100\t Acc@5 35.3800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:58:32,148 - INFO - Head 38.278\tMid 11.971\tTail 0.483\u001b[0m\n",
      "\u001b[32m2024-10-06 15:58:32,148 - INFO - epoch: 108 | train loss: 4.1751 | train accuracy: 99.770 | test loss: 3.9241 | test accuracy: 18.110 | epoch runtime:   3.92 sec | best accuracy: 18.430 @ epoch: 097\u001b[0m\n",
      "\u001b[32m2024-10-06 15:58:36,048 - INFO - Evaluate Summary Time 1.72s\tLoss 3.9149\t Acc@1 18.2600\t Acc@5 35.6000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:58:36,048 - INFO - Head 38.583\tMid 12.029\tTail 0.552\u001b[0m\n",
      "\u001b[32m2024-10-06 15:58:36,049 - INFO - epoch: 109 | train loss: 4.1723 | train accuracy: 99.825 | test loss: 3.9149 | test accuracy: 18.260 | epoch runtime:   3.90 sec | best accuracy: 18.430 @ epoch: 097\u001b[0m\n",
      "\u001b[32m2024-10-06 15:58:39,980 - INFO - Evaluate Summary Time 1.75s\tLoss 3.9209\t Acc@1 18.3400\t Acc@5 35.3800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:58:39,980 - INFO - Head 38.722\tMid 12.143\tTail 0.517\u001b[0m\n",
      "\u001b[32m2024-10-06 15:58:39,981 - INFO - epoch: 110 | train loss: 4.1709 | train accuracy: 99.788 | test loss: 3.9209 | test accuracy: 18.340 | epoch runtime:   3.93 sec | best accuracy: 18.430 @ epoch: 097\u001b[0m\n",
      "\u001b[32m2024-10-06 15:58:43,825 - INFO - Evaluate Summary Time 1.72s\tLoss 3.9278\t Acc@1 18.5600\t Acc@5 35.4400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:58:43,826 - INFO - Head 39.222\tMid 12.286\tTail 0.483\u001b[0m\n",
      "\u001b[32m2024-10-06 15:58:43,826 - INFO - epoch: 111 | train loss: 4.1671 | train accuracy: 99.751 | test loss: 3.9278 | test accuracy: 18.560 | epoch runtime:   3.85 sec | best accuracy: 18.560 @ epoch: 111\u001b[0m\n",
      "\u001b[32m2024-10-06 15:58:47,749 - INFO - Evaluate Summary Time 1.76s\tLoss 3.9263\t Acc@1 18.1200\t Acc@5 35.3800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:58:47,749 - INFO - Head 38.778\tMid 11.486\tTail 0.483\u001b[0m\n",
      "\u001b[32m2024-10-06 15:58:47,750 - INFO - epoch: 112 | train loss: 4.1700 | train accuracy: 99.852 | test loss: 3.9263 | test accuracy: 18.120 | epoch runtime:   3.92 sec | best accuracy: 18.560 @ epoch: 111\u001b[0m\n",
      "\u001b[32m2024-10-06 15:58:51,653 - INFO - Evaluate Summary Time 1.79s\tLoss 3.9268\t Acc@1 18.3000\t Acc@5 35.2700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:58:51,654 - INFO - Head 39.250\tMid 11.514\tTail 0.483\u001b[0m\n",
      "\u001b[32m2024-10-06 15:58:51,654 - INFO - epoch: 113 | train loss: 4.1695 | train accuracy: 99.797 | test loss: 3.9268 | test accuracy: 18.300 | epoch runtime:   3.90 sec | best accuracy: 18.560 @ epoch: 111\u001b[0m\n",
      "\u001b[32m2024-10-06 15:58:55,615 - INFO - Evaluate Summary Time 1.75s\tLoss 3.9358\t Acc@1 18.0100\t Acc@5 35.0400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:58:55,615 - INFO - Head 38.611\tMid 11.229\tTail 0.621\u001b[0m\n",
      "\u001b[32m2024-10-06 15:58:55,616 - INFO - epoch: 114 | train loss: 4.1668 | train accuracy: 99.834 | test loss: 3.9358 | test accuracy: 18.010 | epoch runtime:   3.96 sec | best accuracy: 18.560 @ epoch: 111\u001b[0m\n",
      "\u001b[32m2024-10-06 15:58:59,493 - INFO - Evaluate Summary Time 1.75s\tLoss 3.9165\t Acc@1 18.1600\t Acc@5 35.3400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:58:59,494 - INFO - Head 38.222\tMid 12.057\tTail 0.621\u001b[0m\n",
      "\u001b[32m2024-10-06 15:58:59,494 - INFO - epoch: 115 | train loss: 4.1681 | train accuracy: 99.825 | test loss: 3.9165 | test accuracy: 18.160 | epoch runtime:   3.88 sec | best accuracy: 18.560 @ epoch: 111\u001b[0m\n",
      "\u001b[32m2024-10-06 15:59:03,298 - INFO - Evaluate Summary Time 1.66s\tLoss 3.9229\t Acc@1 18.1100\t Acc@5 35.1300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:59:03,298 - INFO - Head 38.806\tMid 11.371\tTail 0.552\u001b[0m\n",
      "\u001b[32m2024-10-06 15:59:03,298 - INFO - epoch: 116 | train loss: 4.1676 | train accuracy: 99.862 | test loss: 3.9229 | test accuracy: 18.110 | epoch runtime:   3.80 sec | best accuracy: 18.560 @ epoch: 111\u001b[0m\n",
      "\u001b[32m2024-10-06 15:59:07,192 - INFO - Evaluate Summary Time 1.72s\tLoss 3.9260\t Acc@1 18.1500\t Acc@5 35.2900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:59:07,192 - INFO - Head 38.528\tMid 11.743\tTail 0.586\u001b[0m\n",
      "\u001b[32m2024-10-06 15:59:07,193 - INFO - epoch: 117 | train loss: 4.1633 | train accuracy: 99.806 | test loss: 3.9260 | test accuracy: 18.150 | epoch runtime:   3.89 sec | best accuracy: 18.560 @ epoch: 111\u001b[0m\n",
      "\u001b[32m2024-10-06 15:59:10,930 - INFO - Evaluate Summary Time 1.64s\tLoss 3.9247\t Acc@1 18.4500\t Acc@5 35.3700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:59:10,930 - INFO - Head 39.111\tMid 12.029\tTail 0.552\u001b[0m\n",
      "\u001b[32m2024-10-06 15:59:10,931 - INFO - epoch: 118 | train loss: 4.1665 | train accuracy: 99.843 | test loss: 3.9247 | test accuracy: 18.450 | epoch runtime:   3.74 sec | best accuracy: 18.560 @ epoch: 111\u001b[0m\n",
      "\u001b[32m2024-10-06 15:59:14,772 - INFO - Evaluate Summary Time 1.73s\tLoss 3.9293\t Acc@1 18.2000\t Acc@5 35.1100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:59:14,772 - INFO - Head 38.750\tMid 11.714\tTail 0.517\u001b[0m\n",
      "\u001b[32m2024-10-06 15:59:14,772 - INFO - epoch: 119 | train loss: 4.1657 | train accuracy: 99.816 | test loss: 3.9293 | test accuracy: 18.200 | epoch runtime:   3.84 sec | best accuracy: 18.560 @ epoch: 111\u001b[0m\n",
      "\u001b[32m2024-10-06 15:59:18,575 - INFO - Evaluate Summary Time 1.70s\tLoss 3.9228\t Acc@1 18.1900\t Acc@5 35.3700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:59:18,576 - INFO - Head 38.056\tMid 12.429\tTail 0.483\u001b[0m\n",
      "\u001b[32m2024-10-06 15:59:18,576 - INFO - epoch: 120 | train loss: 4.1630 | train accuracy: 99.834 | test loss: 3.9228 | test accuracy: 18.190 | epoch runtime:   3.80 sec | best accuracy: 18.560 @ epoch: 111\u001b[0m\n",
      "\u001b[32m2024-10-06 15:59:22,570 - INFO - Evaluate Summary Time 1.74s\tLoss 3.9304\t Acc@1 18.1400\t Acc@5 34.9100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:59:22,570 - INFO - Head 38.306\tMid 12.000\tTail 0.517\u001b[0m\n",
      "\u001b[32m2024-10-06 15:59:22,570 - INFO - epoch: 121 | train loss: 4.1636 | train accuracy: 99.871 | test loss: 3.9304 | test accuracy: 18.140 | epoch runtime:   3.99 sec | best accuracy: 18.560 @ epoch: 111\u001b[0m\n",
      "\u001b[32m2024-10-06 15:59:26,753 - INFO - Evaluate Summary Time 1.80s\tLoss 3.9327\t Acc@1 18.3000\t Acc@5 35.3500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:59:26,754 - INFO - Head 38.639\tMid 12.143\tTail 0.483\u001b[0m\n",
      "\u001b[32m2024-10-06 15:59:26,754 - INFO - epoch: 122 | train loss: 4.1639 | train accuracy: 99.816 | test loss: 3.9327 | test accuracy: 18.300 | epoch runtime:   4.18 sec | best accuracy: 18.560 @ epoch: 111\u001b[0m\n",
      "\u001b[32m2024-10-06 15:59:30,532 - INFO - Evaluate Summary Time 1.69s\tLoss 3.9245\t Acc@1 18.1400\t Acc@5 35.3100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:59:30,533 - INFO - Head 38.306\tMid 12.000\tTail 0.517\u001b[0m\n",
      "\u001b[32m2024-10-06 15:59:30,533 - INFO - epoch: 123 | train loss: 4.1620 | train accuracy: 99.908 | test loss: 3.9245 | test accuracy: 18.140 | epoch runtime:   3.78 sec | best accuracy: 18.560 @ epoch: 111\u001b[0m\n",
      "\u001b[32m2024-10-06 15:59:34,413 - INFO - Evaluate Summary Time 1.73s\tLoss 3.9191\t Acc@1 18.2100\t Acc@5 35.3500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:59:34,413 - INFO - Head 38.361\tMid 12.114\tTail 0.552\u001b[0m\n",
      "\u001b[32m2024-10-06 15:59:34,413 - INFO - epoch: 124 | train loss: 4.1596 | train accuracy: 99.843 | test loss: 3.9191 | test accuracy: 18.210 | epoch runtime:   3.88 sec | best accuracy: 18.560 @ epoch: 111\u001b[0m\n",
      "\u001b[32m2024-10-06 15:59:38,546 - INFO - Evaluate Summary Time 1.85s\tLoss 3.9384\t Acc@1 18.1300\t Acc@5 35.0900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:59:38,547 - INFO - Head 38.694\tMid 11.600\tTail 0.483\u001b[0m\n",
      "\u001b[32m2024-10-06 15:59:38,547 - INFO - epoch: 125 | train loss: 4.1633 | train accuracy: 99.834 | test loss: 3.9384 | test accuracy: 18.130 | epoch runtime:   4.13 sec | best accuracy: 18.560 @ epoch: 111\u001b[0m\n",
      "\u001b[32m2024-10-06 15:59:42,679 - INFO - Evaluate Summary Time 1.86s\tLoss 3.9112\t Acc@1 18.2300\t Acc@5 35.6200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:59:42,679 - INFO - Head 38.361\tMid 12.200\tTail 0.517\u001b[0m\n",
      "\u001b[32m2024-10-06 15:59:42,679 - INFO - epoch: 126 | train loss: 4.1629 | train accuracy: 99.825 | test loss: 3.9112 | test accuracy: 18.230 | epoch runtime:   4.13 sec | best accuracy: 18.560 @ epoch: 111\u001b[0m\n",
      "\u001b[32m2024-10-06 15:59:46,732 - INFO - Evaluate Summary Time 1.88s\tLoss 3.9234\t Acc@1 18.2800\t Acc@5 35.3900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:59:46,732 - INFO - Head 38.333\tMid 12.286\tTail 0.621\u001b[0m\n",
      "\u001b[32m2024-10-06 15:59:46,733 - INFO - epoch: 127 | train loss: 4.1616 | train accuracy: 99.899 | test loss: 3.9234 | test accuracy: 18.280 | epoch runtime:   4.05 sec | best accuracy: 18.560 @ epoch: 111\u001b[0m\n",
      "\u001b[32m2024-10-06 15:59:50,697 - INFO - Evaluate Summary Time 1.83s\tLoss 3.9327\t Acc@1 18.3300\t Acc@5 35.2800\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:59:50,698 - INFO - Head 39.000\tMid 11.886\tTail 0.448\u001b[0m\n",
      "\u001b[32m2024-10-06 15:59:50,698 - INFO - epoch: 128 | train loss: 4.1617 | train accuracy: 99.889 | test loss: 3.9327 | test accuracy: 18.330 | epoch runtime:   3.97 sec | best accuracy: 18.560 @ epoch: 111\u001b[0m\n",
      "\u001b[32m2024-10-06 15:59:54,705 - INFO - Evaluate Summary Time 1.86s\tLoss 3.9215\t Acc@1 18.2900\t Acc@5 35.2700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:59:54,705 - INFO - Head 38.528\tMid 12.200\tTail 0.517\u001b[0m\n",
      "\u001b[32m2024-10-06 15:59:54,705 - INFO - epoch: 129 | train loss: 4.1575 | train accuracy: 99.871 | test loss: 3.9215 | test accuracy: 18.290 | epoch runtime:   4.01 sec | best accuracy: 18.560 @ epoch: 111\u001b[0m\n",
      "\u001b[32m2024-10-06 15:59:58,602 - INFO - Evaluate Summary Time 1.78s\tLoss 3.9249\t Acc@1 18.2300\t Acc@5 35.3400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 15:59:58,602 - INFO - Head 38.972\tMid 11.571\tTail 0.517\u001b[0m\n",
      "\u001b[32m2024-10-06 15:59:58,603 - INFO - epoch: 130 | train loss: 4.1591 | train accuracy: 99.816 | test loss: 3.9249 | test accuracy: 18.230 | epoch runtime:   3.90 sec | best accuracy: 18.560 @ epoch: 111\u001b[0m\n",
      "\u001b[32m2024-10-06 16:00:02,426 - INFO - Evaluate Summary Time 1.67s\tLoss 3.9256\t Acc@1 18.1600\t Acc@5 35.4100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 16:00:02,427 - INFO - Head 38.083\tMid 12.286\tTail 0.517\u001b[0m\n",
      "\u001b[32m2024-10-06 16:00:02,427 - INFO - epoch: 131 | train loss: 4.1596 | train accuracy: 99.889 | test loss: 3.9256 | test accuracy: 18.160 | epoch runtime:   3.82 sec | best accuracy: 18.560 @ epoch: 111\u001b[0m\n",
      "\u001b[32m2024-10-06 16:00:06,388 - INFO - Evaluate Summary Time 1.75s\tLoss 3.9186\t Acc@1 18.2700\t Acc@5 35.5500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 16:00:06,388 - INFO - Head 38.500\tMid 12.114\tTail 0.586\u001b[0m\n",
      "\u001b[32m2024-10-06 16:00:06,389 - INFO - epoch: 132 | train loss: 4.1594 | train accuracy: 99.843 | test loss: 3.9186 | test accuracy: 18.270 | epoch runtime:   3.96 sec | best accuracy: 18.560 @ epoch: 111\u001b[0m\n",
      "\u001b[32m2024-10-06 16:00:10,383 - INFO - Evaluate Summary Time 1.79s\tLoss 3.9234\t Acc@1 18.3700\t Acc@5 35.4200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 16:00:10,383 - INFO - Head 38.639\tMid 12.286\tTail 0.552\u001b[0m\n",
      "\u001b[32m2024-10-06 16:00:10,384 - INFO - epoch: 133 | train loss: 4.1577 | train accuracy: 99.871 | test loss: 3.9234 | test accuracy: 18.370 | epoch runtime:   3.99 sec | best accuracy: 18.560 @ epoch: 111\u001b[0m\n",
      "\u001b[32m2024-10-06 16:00:14,236 - INFO - Evaluate Summary Time 1.69s\tLoss 3.9172\t Acc@1 18.3100\t Acc@5 35.3400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 16:00:14,237 - INFO - Head 38.083\tMid 12.657\tTail 0.586\u001b[0m\n",
      "\u001b[32m2024-10-06 16:00:14,237 - INFO - epoch: 134 | train loss: 4.1584 | train accuracy: 99.917 | test loss: 3.9172 | test accuracy: 18.310 | epoch runtime:   3.85 sec | best accuracy: 18.560 @ epoch: 111\u001b[0m\n",
      "\u001b[32m2024-10-06 16:00:18,102 - INFO - Evaluate Summary Time 1.72s\tLoss 3.9309\t Acc@1 18.1300\t Acc@5 35.2400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 16:00:18,102 - INFO - Head 38.694\tMid 11.600\tTail 0.483\u001b[0m\n",
      "\u001b[32m2024-10-06 16:00:18,103 - INFO - epoch: 135 | train loss: 4.1568 | train accuracy: 99.852 | test loss: 3.9309 | test accuracy: 18.130 | epoch runtime:   3.87 sec | best accuracy: 18.560 @ epoch: 111\u001b[0m\n",
      "\u001b[32m2024-10-06 16:00:21,953 - INFO - Evaluate Summary Time 1.63s\tLoss 3.9199\t Acc@1 18.2100\t Acc@5 35.5300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 16:00:21,954 - INFO - Head 38.222\tMid 12.257\tTail 0.552\u001b[0m\n",
      "\u001b[32m2024-10-06 16:00:21,954 - INFO - epoch: 136 | train loss: 4.1562 | train accuracy: 99.899 | test loss: 3.9199 | test accuracy: 18.210 | epoch runtime:   3.85 sec | best accuracy: 18.560 @ epoch: 111\u001b[0m\n",
      "\u001b[32m2024-10-06 16:00:25,913 - INFO - Evaluate Summary Time 1.75s\tLoss 3.9268\t Acc@1 18.2400\t Acc@5 35.2500\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 16:00:25,913 - INFO - Head 38.444\tMid 12.114\tTail 0.552\u001b[0m\n",
      "\u001b[32m2024-10-06 16:00:25,914 - INFO - epoch: 137 | train loss: 4.1619 | train accuracy: 99.926 | test loss: 3.9268 | test accuracy: 18.240 | epoch runtime:   3.96 sec | best accuracy: 18.560 @ epoch: 111\u001b[0m\n",
      "\u001b[32m2024-10-06 16:00:29,809 - INFO - Evaluate Summary Time 1.76s\tLoss 3.9239\t Acc@1 18.2000\t Acc@5 35.2300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 16:00:29,809 - INFO - Head 38.417\tMid 12.057\tTail 0.517\u001b[0m\n",
      "\u001b[32m2024-10-06 16:00:29,809 - INFO - epoch: 138 | train loss: 4.1548 | train accuracy: 99.880 | test loss: 3.9239 | test accuracy: 18.200 | epoch runtime:   3.90 sec | best accuracy: 18.560 @ epoch: 111\u001b[0m\n",
      "\u001b[32m2024-10-06 16:00:33,917 - INFO - Evaluate Summary Time 1.86s\tLoss 3.9234\t Acc@1 18.0600\t Acc@5 35.0700\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 16:00:33,917 - INFO - Head 38.194\tMid 11.857\tTail 0.552\u001b[0m\n",
      "\u001b[32m2024-10-06 16:00:33,918 - INFO - epoch: 139 | train loss: 4.1539 | train accuracy: 99.871 | test loss: 3.9234 | test accuracy: 18.060 | epoch runtime:   4.11 sec | best accuracy: 18.560 @ epoch: 111\u001b[0m\n",
      "\u001b[32m2024-10-06 16:00:38,006 - INFO - Evaluate Summary Time 1.84s\tLoss 3.9214\t Acc@1 18.0800\t Acc@5 35.4400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 16:00:38,007 - INFO - Head 38.139\tMid 11.886\tTail 0.655\u001b[0m\n",
      "\u001b[32m2024-10-06 16:00:38,007 - INFO - epoch: 140 | train loss: 4.1568 | train accuracy: 99.880 | test loss: 3.9214 | test accuracy: 18.080 | epoch runtime:   4.09 sec | best accuracy: 18.560 @ epoch: 111\u001b[0m\n",
      "\u001b[32m2024-10-06 16:00:41,953 - INFO - Evaluate Summary Time 1.75s\tLoss 3.9317\t Acc@1 18.1300\t Acc@5 35.1100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 16:00:41,954 - INFO - Head 37.944\tMid 12.314\tTail 0.552\u001b[0m\n",
      "\u001b[32m2024-10-06 16:00:41,954 - INFO - epoch: 141 | train loss: 4.1549 | train accuracy: 99.871 | test loss: 3.9317 | test accuracy: 18.130 | epoch runtime:   3.95 sec | best accuracy: 18.560 @ epoch: 111\u001b[0m\n",
      "\u001b[32m2024-10-06 16:00:45,908 - INFO - Evaluate Summary Time 1.71s\tLoss 3.9281\t Acc@1 18.1900\t Acc@5 35.2900\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 16:00:45,908 - INFO - Head 38.667\tMid 11.743\tTail 0.552\u001b[0m\n",
      "\u001b[32m2024-10-06 16:00:45,909 - INFO - epoch: 142 | train loss: 4.1563 | train accuracy: 99.880 | test loss: 3.9281 | test accuracy: 18.190 | epoch runtime:   3.95 sec | best accuracy: 18.560 @ epoch: 111\u001b[0m\n",
      "\u001b[32m2024-10-06 16:00:49,891 - INFO - Evaluate Summary Time 1.80s\tLoss 3.9211\t Acc@1 18.2900\t Acc@5 35.4100\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 16:00:49,891 - INFO - Head 38.778\tMid 11.886\tTail 0.586\u001b[0m\n",
      "\u001b[32m2024-10-06 16:00:49,891 - INFO - epoch: 143 | train loss: 4.1554 | train accuracy: 99.926 | test loss: 3.9211 | test accuracy: 18.290 | epoch runtime:   3.98 sec | best accuracy: 18.560 @ epoch: 111\u001b[0m\n",
      "\u001b[32m2024-10-06 16:00:53,832 - INFO - Evaluate Summary Time 1.73s\tLoss 3.9233\t Acc@1 18.3700\t Acc@5 35.3200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 16:00:53,833 - INFO - Head 38.972\tMid 11.943\tTail 0.552\u001b[0m\n",
      "\u001b[32m2024-10-06 16:00:53,833 - INFO - epoch: 144 | train loss: 4.1566 | train accuracy: 99.871 | test loss: 3.9233 | test accuracy: 18.370 | epoch runtime:   3.94 sec | best accuracy: 18.560 @ epoch: 111\u001b[0m\n",
      "\u001b[32m2024-10-06 16:00:57,777 - INFO - Evaluate Summary Time 1.74s\tLoss 3.9268\t Acc@1 18.3000\t Acc@5 35.1400\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 16:00:57,777 - INFO - Head 38.611\tMid 12.200\tTail 0.448\u001b[0m\n",
      "\u001b[32m2024-10-06 16:00:57,777 - INFO - epoch: 145 | train loss: 4.1538 | train accuracy: 99.880 | test loss: 3.9268 | test accuracy: 18.300 | epoch runtime:   3.94 sec | best accuracy: 18.560 @ epoch: 111\u001b[0m\n",
      "\u001b[32m2024-10-06 16:01:01,630 - INFO - Evaluate Summary Time 1.66s\tLoss 3.9172\t Acc@1 18.0200\t Acc@5 35.2000\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 16:01:01,630 - INFO - Head 37.333\tMid 12.571\tTail 0.621\u001b[0m\n",
      "\u001b[32m2024-10-06 16:01:01,630 - INFO - epoch: 146 | train loss: 4.1539 | train accuracy: 99.871 | test loss: 3.9172 | test accuracy: 18.020 | epoch runtime:   3.85 sec | best accuracy: 18.560 @ epoch: 111\u001b[0m\n",
      "\u001b[32m2024-10-06 16:01:05,660 - INFO - Evaluate Summary Time 1.79s\tLoss 3.9221\t Acc@1 18.1300\t Acc@5 35.4300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 16:01:05,660 - INFO - Head 37.861\tMid 12.343\tTail 0.621\u001b[0m\n",
      "\u001b[32m2024-10-06 16:01:05,661 - INFO - epoch: 147 | train loss: 4.1563 | train accuracy: 99.908 | test loss: 3.9221 | test accuracy: 18.130 | epoch runtime:   4.03 sec | best accuracy: 18.560 @ epoch: 111\u001b[0m\n",
      "\u001b[32m2024-10-06 16:01:09,669 - INFO - Evaluate Summary Time 1.77s\tLoss 3.9267\t Acc@1 18.1500\t Acc@5 35.1300\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 16:01:09,670 - INFO - Head 38.278\tMid 12.000\tTail 0.586\u001b[0m\n",
      "\u001b[32m2024-10-06 16:01:09,670 - INFO - epoch: 148 | train loss: 4.1557 | train accuracy: 99.917 | test loss: 3.9267 | test accuracy: 18.150 | epoch runtime:   4.01 sec | best accuracy: 18.560 @ epoch: 111\u001b[0m\n",
      "\u001b[32m2024-10-06 16:01:13,524 - INFO - Evaluate Summary Time 1.71s\tLoss 3.9394\t Acc@1 18.3500\t Acc@5 35.0200\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 16:01:13,525 - INFO - Head 39.250\tMid 11.657\tTail 0.483\u001b[0m\n",
      "\u001b[32m2024-10-06 16:01:13,525 - INFO - epoch: 149 | train loss: 4.1532 | train accuracy: 99.889 | test loss: 3.9394 | test accuracy: 18.350 | epoch runtime:   3.85 sec | best accuracy: 18.560 @ epoch: 111\u001b[0m\n",
      "\u001b[32m2024-10-06 16:01:17,510 - INFO - Evaluate Summary Time 1.80s\tLoss 3.9200\t Acc@1 18.2400\t Acc@5 35.2600\u001b[0m                                       \n",
      "\u001b[32m2024-10-06 16:01:17,510 - INFO - Head 38.167\tMid 12.457\tTail 0.483\u001b[0m\n",
      "\u001b[32m2024-10-06 16:01:17,511 - INFO - epoch: 150 | train loss: 4.1539 | train accuracy: 99.899 | test loss: 3.9200 | test accuracy: 18.240 | epoch runtime:   3.99 sec | best accuracy: 18.560 @ epoch: 111\u001b[0m\n",
      "Runtime of this script /home/zyx/zhengjinpeng/PNP/cifar.py : 590.4 seconds (0.164 hours)\n"
     ]
    }
   ],
   "source": [
    "# !python cifar.py --config ./config/cifar100.cfg\n",
    "# !python cifar.py --config ./config/cifar100.cfg --r_ood 0.2\n",
    "!python cifar.py --config ./config/cifar100.cfg --r_ood 0.2 --closeset-ratio 0.2\n",
    "!python cifar.py --config ./config/cifar100.cfg --r_ood 0.2 --closeset-ratio 0.2 --noise-type asymmetric\n",
    "# !python cifar.py --config ./config/cifar100.cfg --r_imb 0.01\n",
    "# !python cifar.py --config ./config/cifar100.cfg --r_imb 0.01 --r_ood 0.2\n",
    "!python cifar.py --config ./config/cifar100.cfg --r_imb 0.01 --r_ood 0.2 --closeset-ratio 0.2\n",
    "!python cifar.py --config ./config/cifar100.cfg --r_imb 0.01 --r_ood 0.2 --closeset-ratio 0.2 --noise-type asymmetric"
   ]
  }
 ],
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